当前位置：文档库 › 施工仿真 (257)

施工仿真 (257)

Simulation-based safety evaluation model integrated

with network schedule

Wei-Chih Wang *,Jang-Jeng Liu,Shih-Chieh Chou

Department of Civil Engineering,National Chiao Tung University,1001,Ta-Hsueh Road,Hsin-Chu 300,Taiwan

Accepted 7June 2005

Abstract

Construction accidents often lead to project delays.However,in practice,construction safety and schedule control are managed separately.This work develops an innovative simulation-based model,SimSAFE,that assesses the hazard (or expected accident costs)for each activity in a network schedule.Thus,at any time point,safety managers can pay considerable attention to activities (or paths or working zones)with high expected accident costs.Additionally,by breaking down the uncertainty of an accident cause occurring,SimSAFE provides factor-sensitivity information to support safety risk management.Enhancing knowledge of the safety factors (such as safety training and site environment)to which an activity (or path or zone)is sensitive,and also of the activities (or paths or zones)that are most sensitive to a particular factor can provide management with a better sense of what factors and activities (or paths or zones)need to be controlled for reducing construction accidents,especially for a large project or multiple projects.D 2005Elsevier B.V .All rights reserved.

Keywords:Construction safety;Accident cost;Schedule control;Uncertainty;Simulation

1.Introduction

The construction industry is statistically one of the most hazardous industries in many countries [1–3].For example,in Taiwan,approximately 60%of fatal accidents in all industries between 1999and 2001arose in the construction industry [4].Besides causing human tragedy,construction accidents also delay project progress,increase costs,and damage the reputation of the contractors.Therefore,appropriate safety planning that meets governmental safety regulations is an essential task before commencing con-struction work.During construction,contractors are asked to employ qualified safety specialists,assemble temporary safety facilities (for example,falsework and electricity),provide safety machinery/equipment (for example,cranes and excavators),supply safeguards (for example,safety signals,safety nets,and fire extinguishers),provide personal

protective equipment (for example,hard hats,safety shoes,safety belts,and hearing protection),and provide workers with adequate safety instructions before allowing them on the jobsite.

However,most contractors simply see their safety plans as a burdensome necessity for avoiding government fines,and neglect their implementation [5].The implementation of safety plans is frequently regarded as an extra task.Several safety professionals have realized that improving safety performance requires integrating safety with construction planning and control [5,6].Namely,safety management must be treated with the same kind of thoughtful project planning and control that goes into other aspects of project management.

This study proposes a simulation-based model,Sim-SAFE,that incorporates safety management into schedule control.Specifically,the degree of hazard (or expected accident costs)of each activity in a construction project is evaluated;and this evaluation information is attached to the project network schedule.Simulation algorithms are used to consider uncertainties (uncertain safety factors)that are

*Corresponding author.Tel.:+88635712121x54952;fax:+88635716257.

E-mail address:weichih@https://www.wendangku.net/doc/259912797.html,.tw (W.-C.Wang).Automation in Construction 15(2006)341–

354

https://www.wendangku.net/doc/259912797.html,/locate/autcon

often ignored in safety management.The anticipated advantages of attaching safety information to project schedules are to increase knowledge of the factors to which an activity(or path or zone)is sensitive,and those that are most sensitive to a particular factor.As a result,manage-ment can better understand what factors relating to each activity(or path or zone)and of what activities(or paths or zones)to control to reduce construction accidents.

The methodology used in this investigation comprises the following phases:(1)review relevant research to identify the focuses of existing studies;(2)define the factors and the degree of hazard of construction activities;

(3)present the SimSAFE model to evaluate the degree of hazard of each activity of a network schedule;(4)illustrate the detailed modeling steps using a two-activity network;

(5)demonstrate model operation through application to an example project;and(6)elucidate the advantages of the model and recommend future research directions.

2.Pertinent research

Extensive research has been conducted to improve construction safety.It can be separated into the following seven categories.

Identifying root causes of injuries—for example,Hinze et al.suggested coding injuries into one of20possible categories of accident causes,instead of the conventional five groups of falls,struck-by,electric shocks,caught in/ between,and others[7].

Identifying factors or strategies that influence safety performance—in the UK,Sawacha et al.identified that the five most important factors or strategies associated with site safety were management talks on safety, provision of safety booklets,provision of safety equip-ment,provision of safety environment,and appointment of a trained safety representative on site[8].Using a questionnaire survey,Fang et al.analyzed the correla-tions between the safety factors and safety management performance[9].

Examining the usefulness of various safety performance measures—considering the effectiveness of a method of measuring safety performance,Laufer and Ledbetter investigated various measurement methods and con-cluded that the most effective measures were lost-day cases,doctor’s cases,and cost of accidents[10].Hinze et al.focused on a widely-used method,experience modification rating(EMR),for clarifying the effect of different variables on the EMR values[11].Their results demonstrated that injury frequency impacted the EMR computation more than injury severity.Based on a survey by de la Garza et al.[12],‘‘what gets measured,gets improved.’’

Designing strategies for improving safety performance—using a behavioral approach,Duff et al.showed that

safety behavior can be objectively measured;goal setting and feedback interventions could significantly improve safety performance;and commitment of site managers could enhance intervention effectiveness[13].Owing to the limitations of each measure of safety performance (including EMR and recordable incident rate),Jaselskis et al.created a questionnaire asking questions regarding the combination of measures that gave the best overall indication of safety performance at both the company and project levels[14].

Estimating the costs of accidents and injuries—Hinze and Appelgate displayed that the indirect costs often substantially exceeded the direct costs for construction injuries[15].Everett and Frank demonstrated that the costs associated with accidents and injuries have risen from6.5%of construction costs in1982to between7.9% and15%in1995[16].Meanwhile,indirect costs,which were less tangible,included those costs associated with loss of productivity,administrative time for investiga-tions and reports,cleanup,repairs,etc.

Addressing construction worker safety in the design phase—considering that designers generally lack the relevant knowledge and thus their involvement in construction safety is limited,Gambatese and Hinze accumulated design suggestions for developing a tool to assist designers in identifying project-specific safety hazards and providing suitable practices for eliminating construction accidents[17].

Integrating safety concerns with other management plans—for instance,Saurin et al.devised a model to integrate safety into three hierarchical levels(namely, long-term,medium-term,and short-term)of production planning[5].Long-term safety planning started with the preliminary hazard analysis of construction processes.

The long-term plans were updated and detailed at both medium(tri-weekly)-and short-term(daily or weekly) planning levels.The major performance measure adopted for safety assessment during the short-term was the percentage of work packages that were completed safely.

Kartam designed a framework for a computerized safety and heath knowledge-intensive system that was inte-grated with current critical-path-method(CPM)schedul-ing software[6].In this framework,extensive safety data and knowledge(including knowledge required by law and gathered from professionals)were coded and stored in a database system which was linked to CPM project files.

In summary,the area of research that is most relevant to this work concerns the integration of safety considerations into management plans.The model of Saurin et https://www.wendangku.net/doc/259912797.html,bines safety control functions with existing production planning and control processes.They regarded safety planning and control as a broad managerial process[5].However,as stated by Saurin et al.[5],their model did not formally evaluate the uncertainty in the occurrence of causes of

W.-C.Wang et al./Automation in Construction15(2006)341–354 342

accidents.Also,their safety planning and control were not integrated with schedule control.The framework proposed by Kartam sought primarily to reduce the time spent searching through volumes of safety regulations using a computerized system,allowing safety information specific to individual activities to be accessed[6].Namely,the framework did not provide a proactive safety alert(to indicate which activities are more hazardous than others).

3.Factors and degrees of hazard

During construction activity execution,one or several accident causes(simply termed‘‘causes’’herein)are likely to occur.Several factors influence the likelihood of a cause occurring during the execution of a particular activity.If factor performance strongly influences the likelihood of a cause occurring during an activity,then management had better pay attention to the effective management of the factor for the activity.Typical safety factors include safety training,site environment,subcontractor safety management ability,and safety inspection.For instance,the likelihood of occurrence of‘‘falls from elevated position’’is highly sensitive to the safety training of the workers who perform the steel-erection https://www.wendangku.net/doc/259912797.html,ly,inexperienced or poorly trained workers likely lack the safety knowledge required to minimize the risk of falling from high working places.Since factor performance is uncertain,this work treats factors as uncertainty variables.

No accidents occur without there first being a cause. When there is a cause(for example,a laborer falls from an elevation),workers may suffer different degrees of injury (such as,light injury,medium injury,severe injury, disabling injury,or death)and additional costs may be required to deal with the accident.These resulting costs are here called accident costs.(The accident costs are detailed in Section4.2.1.)

The expected accident cost of activity i due to various causes is termed the degree of hazard of activity i,denoted as H i;and is represented as

H i?h i1eTth i2eTtNth i jeTtNth i JeTe1Tin which h i(j)denotes the degree of hazard(expected accident cost)resulting from cause j(j=1,...,J)for activity i.In Taiwan,15categories of causes(categories A–O)are commonly used to record accident data.The categories of causes include falls from elevation(A),falls from ground level(B),collisions(C),strikes from falling materials(D), strikes from collapsed objects(E),strikes from equipment

(F),caught in/between(trapping by)equipment or material

(G),stabbing or slashing(H),trampling(I),burning by high or low temperature materials(J),poisoning by toxic substances(K),electric shock(L),explosion(M),striking by breaking objects(N),and fire(O).

4.SimSAFE model

SimSAFE evaluates the degree of hazard of each activity according to the following four steps(see Fig.1):(1) evaluating the likelihood of each cause occurring;(2) assessing the accident costs associated with each cause;

(3)applying computer simulation for dealing with uncer-tainties;and(4)integrating safety information with the network schedule.The algorithms used for each step are detailed below.

Notably,in this investigation,the occurrence of a cause depends on the‘‘likelihood’’of such a clause occurring. Moreover,this‘‘likelihood’’is a variable represented by a distribution of likelihood(a pool of possible likelihood values).Factors affect the distribution of such a variable.A particular likelihood value is picked in a given run(or iteration),depending on how simulation draws from the distribution.For example,if simulation randomly draws

a Fig.1.Modeling steps of SimSAFE.

W.-C.Wang et al./Automation in Construction15(2006)341–354343

value of 0.6from a distribution as the likelihood of a cause occurring,this value of 0.6then indicates a 60%chance that the cause will occur,and a 40%chance that the cause will not occur.The following section illustrates such a distribu-tion of likelihood.

4.1.Evaluating the likelihood of each cause occurring For each activity,the distribution of the likelihood of a particular cause occurring is determined based on a reference likelihood and the effects of factors on that likelihood.

4.1.1.Establishing the base likelihood for each accident cause

The reference likelihood of a specific cause occurring is derived from historical data.This reference likelihood (REF j )of cause j is defined as the total number of construction workers injured owing to cause j in 1year divided by the total number of construction workers registered with the Taiwan Council of Labor Affairs (CLA)in that year.The Taiwan CLA,the highest governmental agency responsible for occupational safety and health,possesses an annual collection of historical data required to calculate REF j [4].Notably,the reference likelihood of a cause (calculated from historical data)is used as a basis for assisting the model user in evaluating the likelihood of the cause’s occurring in association with a particular activity.Table 1lists the likelihood of each cause occurring,on a per worker basis.

For example,2581workers were injured because of cause A (falls from evaluation)in 2000;and 722,238workers were registered with the CLA during the same year.Therefore,the reference likelihood of cause A occurring per worker is 0.0035736(=2581/722,238).Namely, 3.5736workers could be injured per thousand workers.Activity

execution involves multiple workers.Accordingly,the reference likelihood of cause j should be further multiplied by the number of workers performing the activity.In the above example,if ten workers are involved in an activity,then the likelihood of cause A occurring for that activity equals 0.0035736?10=0.035736.

4.1.2.Deriving an overall distribution of the likelihood for each cause

Three-point estimation is adopted to obtain the overall distribution (F i (j ))of each cause j occurring for an activity i .The mean (M i (j ))and standard deviation (r i (j ))of this overall distribution are derived as [18],

M i j eT?l i j eTt4t i j eTtu i j eT

áà6e2T

r i j eT?

u i j eTàl i j eT

3:2

e3T

where l i (j ),t i (j )and u i (j )denote the optimistic (or low),most likely and pessimistic (or high)likelihoods of F i (j )due to cause j for activity i ,respectively (i =1,2,...,I and j =1,2,...,J ).

The derived reference likelihoods listed in Table 1are used to help establish the distribution of F i (j ).For practical considerations,a multiplier system is proposed to help determine the three-point estimations such that the model user needs only provide qualitative inputs.Table 2shows an example of such a multiplier system including seven multipliers,each corresponding to a particular qualitative estimate.In the previous example,if the qualitative estimate of pessimistic value because of cause A for activity i is determined to be ‘‘higher than’’the reference likelihood,then the derived pessimistic likelihood (u i (j ))=reference likelihood ?number of workers involved ?a corresponding multiplier =0.0035736?10?2.0=0.071472.Notably,the model user can define their multiplier system using varying multiplier values.

4.1.3.Breaking down the overall distribution

Each overall distribution (F i (j ))is disaggregated as the sum of a deterministic base likelihood and a series of zero-mean sub-distributions (called factor distributions)due to various factors.Additionally,each factor distribution with respect to a particular factor is further broken down into a family of several sub-sub-distributions (called factor-con-dition distributions)representing the uncertainty resulting from a specific condition (such as,good,normal,or bad)of the factor.Fig.2illustrates the two breakdowns of uncertainty for F i (j ).Notably,the model user determines the number of factors involved and the number of factor-condition distributions in a family.

Mathematically,F i (j ),a random variable,is represented by [19]

F i j eT?f i j ;0eTtf i j ;1eTtN tf i j ;K eT

e4T

Table 1

Reference likelihood associated with each cause of accidents per worker Accident causes

Number of workers injured in a year Reference likelihood A.Falls from elevation 25810.0035736B.Falls from ground level 11580.0016033C.Collisions

990.0001371D.Strikes from falling material 6200.0008584E.Strikes from collapsed objects 1540.0002132F.Strikes from equipment 4460.0006175G.Caught in/between equipment or material 16440.0022763H.Stabbing or slashing 18920.0026196I.Trampling

190.0000263J.Burning by high or low temperature materials

1670.0002312K.Poisoning by toxic substances/gas 400.0000554L.Electric shock 1160.0001606M.Explosion

350.0000485N.Striking by breaking objects 40.0000055O.Fire

0.0000194

W.-C.Wang et al./Automation in Construction 15(2006)341–354

344

where f i (j ,0)denotes the base likelihood estimated under the expected conditions of all factors.The random variable,f i (j ,k ),is the factor distribution of cause j because of factor k (k =1,...,K )for activity i .

The expected values of the factor distributions are assumed to be zero;m i (j ,1)=m i (j ,2)=...=m i (j ,K )=0.f i (j ,1),f i (j ,2),...,and f i (j ,K )are assumed to be independent of one another.Then,regardless of the statistical distribution of f i (j ,k ),the mean (m i (j ))and variance (r 2i (j ))of F i (j )are as follows [19,20]:

M i j eT?m i j ;0eTtm i j ;1eTtm i j ;2eTtN tm i j ;K eT

?m i j ;0eT

e5T

r 2i j eT?SD 2i j ;0eTtSD 2i j ;1eTtSD 2i j ;2eTtN tSD 2

i j ;K eT

?SD 2i j ;1eTtSD 2i j ;2eTtN tSD 2

i j ;K eT

e6T

where SD 2i (j ,k )denotes the variance of f i (j ,k );SD 2

i (j ,0)=0.Restated,the mean of F i (j )is its base likelihood,and the variance of F i (j )is the sum of the variances of individual factor distributions.

4.1.4.Deriving factor distribution

The overall distribution is broken down into several factor distributions based on subjective information.That is,for each activity,the model user must qualitatively estimate the sensitivity of each factor on each cause occurring.If cause j is highly sensitive to a specific factor k ,a large portion of the variance (r 2i (j ))of the overall distribution due to cause j is then allocated as the variance of the factor distribution owing to factor k .

A scale system is devised for allocating the variance of the overall distribution to the factor https://www.wendangku.net/doc/259912797.html,ly [19,20],

r 2i j eT?SD 2i j ;1eTtSD 2i j ;2eTtN tSD 2

i j ;K eT

e7a T

?w 1Q i j ;1eT??tw 2Q i j ;2eT??tN tw K Q i j ;K eT

??á

à?Y i j eT

e7b TSD 2i j ;k eT

?w k Q i j ;k eT?

??Y i j eTe8T

where Q i (j ,k )denotes the qualitative estimate of cause j for factor k .Moreover,w k [Q i (j ,k )]represents a scale of each

Overall distribution

Factor distribution

Factor-condition distribution

Fig.2.Two breakdowns of the overall distribution of the likelihood associated with each cause.

Table 2

Multiplier system to transfer qualitative estimates of likelihood Qualitative estimates Extremely higher than (EH)Higher than (H)Slightly higher than (SH)Almost the same as (AS)Slightly lower than (SL)Lower than (L)Extremely lower than (EL)Multiplier

2.5

1.5

0.67

0.5

0.4

W.-C.Wang et al./Automation in Construction 15(2006)341–354

345

level of influence.If Q i(j,k)represents a high level of in-fluence,then w k[Q i(j,k)]is great.The relative importance of factors determines the value of w k[Q i(j,k)].Y i(j)is an adjust-ment constant that ensures that the variance(r2i(j))is main-tained.The value of w k[Q i(j,k)]remains the same for each factor and level of influence,so Y i(j)differs for each cause.

4.1.

5.Deriving child distributions

In the second breakdown of uncertainty(see Fig.2),the mean(m i(j,k))and variance(SD2i(j,k))of the factor distribu-tion equal the mean and variance of the combination of the factor-condition distributions for a family,respectively. Mathematically,this relationship is given by[19,20]

m i j;k

eT?0?

X C

c?1

p j;k ceT?o i;j k ceT

e9T

SD2i j;k

eT?

X C

c?1

p j;k ceT?sd2i;j k ceT

to2i;j k ceT

e10T

where C denotes the number of factor-condition distribu-tions,and p j,k(c)represents the probability of occurrence of factor-condition distribution c of factor k for cause j.The mean and standard deviation of the factor-condition distribution c of factor k associated with cause j for activity

i are o i,j[k(c)]and sd i,j[k(c)],respectively.

4.2.Assessing the accident costs associated with each cause

In the event of an accident,the resulting accident cost depends on the type of injury.This section illustrates the accident cost for each type of injury,the chance of each type of injury occurring,and the expected accident cost(the degree of hazard)due to each cause for each activity.

4.2.1.Accident cost for each injury type

The five-type injury classification system used by the Institute of Occupational Safety and Health(IOSH)in Taiwan is adopted in this study.This system classifies injuries as light(represented by t1),medium(t2),severe (t3),disabling(t4),and fatal(t5)[21].Based on the accident cost data presented by IOSH,the average accident costs per accident for injury t(t=t1–t5)for a specific cause j, Cost t(j),are estimated and listed in Table3.Table3lists the accident costs in New Taiwan dollars,hereafter referred to as NT dollars.(1US dollar;32New Taiwan dollars.) Accident costs include both direct and indirect costs.The direct costs were estimated using historical data collected by IOSH between1991and1993[21].Moreover,the direct costs per accident consisted of the compensation(e.g., wages paid to the injured workers for time not worked and consolation money)paid by the contractor to the injured workers,plus any compensation(e.g.,cash payments to the injured workers and medical care)paid by the public liability insurance(namely,the Bureau of Labor Insurance of Taiwan).Notably,the analysis presented in Table3excluded compensation from private insurance companies. The indirect costs per accident included the costs of accident investigation,legal fees,training costs for replacement workers,equipment and materials damage,lost profits, and costs associated with loss of productivity(if any).These costs,which are comparatively difficult to quantify,were estimated by interviews(or sometimes questionnaires)with over70Taiwanese contractors[21].

The accident costs listed in Table3are calculated as follows.For example,the per accident cost of light injury for cause A is

NT$15;000?direct coststindirect costs

?direct costs per lightly injured worker

?average number of workers injured due

to cause ATtindirect costs per accident

?$3000?worker per accident

t12;000:

The figures of$3000and$12,000were obtained from IOSH.Moreover,the average number of workers injured was estimated based on the3-year historical accident data from the Taiwanese Council of Labor Affairs[4].Namely, the average number of workers injured owing to a particular cause was the total number of workers injured due to that cause during a3-year period divided by the number of accidents because of that cause during that period.For example,the average number of workers injured due to cause A was one person per accident.(Notably,the accident cost data listed in Table3can be updated if additional historical data are collected.It is suggested that future research can conduct this updating work.)

4.2.2.Chance of each type of injury arising

The chance of a particular type of injury occurring following the occurrence of a particular cause can vary in executing different activities.For practicality,the model user Table3

Cost of accident of each type of injury for each cause

Causes Accident costs(in thousand,NT dollars)

Light

injury

Medium

injury

Severe

injury

Disabling

injury

Death

A1515846110932143 B101677305382143 C6686653112143 D16.1192.5713.811512294.8 E32.822368116153053.8 F 6.277.2688.42402446.6 G61416533812143 H11502613362143 I9624941682143 J10.51329834482143 K11987573092143 L21.5159.81201657.62294.8 M52.2281.482511873357.4 N21.5193187.5582.52902 O56.3252.174217692598.4

W.-C.Wang et al./Automation in Construction15(2006)341–354 346

qualitatively assesses the chances of various types of injury occurring,then transfers these qualitative assessments to quantitative values.A possible set of quantitative values for various qualitative estimates is very high(VH)=5;high (H)=4;medium(M)=3;low(L)=2;and very low(VL)=1. For example,assume that the chances of light injury,medium injury,severe injury,disabling injury,and fatal injury occurring following the occurrence of cause A in association with a particular activity are qualitatively estimated as follows:VL,L,L,H and VH,respectively.The chances of these five types of injuries occurring following the occur-rence of this cause then are1/14(=1/(1+2+2+4+5)),2/14, 2/14,4/14,and5/14,respectively.Notably,the model user is permitted to define their quantitative values.

Fig.3.Steps of computer implementation of each activity in SimSAFE.

W.-C.Wang et al./Automation in Construction15(2006)341–354347

4.2.3.Expected accident cost for each cause

The expected accident cost owing to cause j in association with activity i,h i(j),is calculated as follows

h i jeT?

X t5

t?t1

Chance i;t jeT?Cost t jeT

e11T

where Chance i,t(j)is the chance of injury t occurring due to j for activity i following a certain number of simulation iterations.See Section4.3.Chance i,t(j)is determined based on the likelihood of a cause j(F i(j))occurring,and the chance of a particular injury t(refer to Section4.2.2)arising in association with activity i.Cost t(j),listed in Table3,is the average cost per accident for injury t given a specific cause j.

https://www.wendangku.net/doc/259912797.html,puter simulation

Fig.3shows the implementation strategy of the SimSAFE model.The following steps are executed during each simulation iteration for activity i.

&The base likelihood(f i(j,0))of each cause j occurring is retrieved.

&A condition is selected for each factor k in association with each cause j based on a specific probability(p j,k(c)) that the condition applies.

&A likelihood sample(f i(j,k))is independently drawn from the factor-condition distribution corresponding to the condition for cause j.

&After processing all the conditions of each factor,the overall distribution(F i(j))associated with each cause j is determined by summing its base likelihood and varia-tions resulting from each factor condition.See Eq.(4). &The probability that an accident will result due to cause j then is F i(j).Meanwhile,the probability of no accident occurring owing to cause j is1àF i(j).The model randomly determines whether an accident will occur because of cause j.

&No accident cost results if no accident occurs due to cause j.If an accident does occur owing to cause j,a particular type of injury t then is determined(see Section

4.2.2).At this step,the selected type of injury resulting

from cause j is recorded.

&The above steps are repeated for all causes.

Following all iterations are considered,the chance (Chance i,t(j))that a particular injury t due to cause j in association with activity i is calculated.Subsequently,the expected accident costs owing to cause j(h i(j))and due to all causes(H i)for each activity can be obtained based on Eqs.(11)and(1),respectively.

A simulation language,Stroboscope[22],was used to implement the simulation-relevant algorithms described herein.Stroboscope can dynamically access simulation state and includes an add-on that enables the definition of CPM networks with stochastic variables(for example,the occur-rence of a cause)and the calculation of various statistics regarding the activities,paths,working zones and project.In this investigation,Stroboscope was run in the Windows2000 environment,with a P3850CPU and256Mega Ram. Approximately1min was required to run1000iterations.

4.4.Integration with CPM network

The simulated likelihood of each cause(F i(j))occurring and the expected accident costs(h i(j)and H i)for each activity are attached to the schedule network.Then,the sensitivities of paths(or zones)to uncertainties are assessed.The uncertainty sensitivity of factor k on a particular path for cause j is measured using the Coefficient of Variation(CV)of the occurrence of an accident along path.A path is considered highly sensitive to a factor if it has a high CV value for that factor.High factor sensitivity along a path indicates that the occurrence of an accident due to cause j for the path is strongly affected by change in that factor.Mathematically, the value of CV of a path for factor k,CV path,k,is

CV path;k?PSD path;k=Mean pathe12Tin which Mean path denotes the mean occurrence of accident due to all causes for all factors along a path,and PSD path,k is the standard deviation of accident occurrence due to all causes along a path when only factor k is assessed.

Similarly,the uncertainty sensitivity for factor k in a working zone(CV zone,k)is as follows.

CV zone;k?ZSD zone;k=Mean zonee13Twhere Mean zone denotes the mean accident occurrence due to all causes for all factors within a zone;and ZSD zone,k represents the standard deviation of accident occurrence due to all causes within a specific zone when only factor k is assessed.

5.Model operation using a two-activity network

This section illustrates the input operations for SimSAFE using a two-activity small network;activities X Y Y.Ten and 15workers are involved in executing activities X and Y, respectively.Five accident causes are considered,including causes A,B,D,E,and L.Moreover,three factors are considered,including factors F1,F2,and F3.

5.1.Inputs

5.1.1.Input1:providing three-point estimations of likelihoods

The model user qualitatively provides the optimistic likelihood(l i(j)),most likely likelihood(t i(j)),and pessi-mistic likelihood(u i(j))for each cause j in association with each activity i.Each qualitative estimate is then transformed to a quantitative value based on a multiplier system listed in

W.-C.Wang et al./Automation in Construction15(2006)341–354 348

Table2.Take cause A for activity X for example.If the qualitative estimates of l i(j),t i(j),and u i(j)are‘‘higher,’’‘‘slightly higher,’’and‘‘slightly lower’’than the reference likelihood,respectively,then the quantitative values of the likelihoods for cause A are0.07147(=2?0.0035736?10), 0.05360(=1.5?0.0035736?10),and0.02394(=0.67?0.0035736?10),respectively.Table4lists the values of l i(j),t i(j),and u i(j)for each cause in association with each activity.Additionally,the mean and standard deviation of the overall distribution are calculated using Eqs.(2)and(3) and are also displayed on the right of Table4.

5.1.2.Input2:providing qualitative factor sensitivities

Table5lists the sensitivity of each factor on the occurrence of each cause for each activity.For example, factors F1,F2and F3have high,low and medium sensitivities to the occurrence of cause A for activity X, respectively.

5.1.3.Input3:estimating the chance of each type of injury arising

Table6lists the qualitative estimates and calculated values of the chance of each type of injury occurring.For example,in cause A,the qualitative estimates of the chances of occurrence for light injury,medium injury,severe injury, disabling injury,and fatal injury are very low(VL),low(L),low(L),high(H)and very high(VH),respectively.Then,

based on a transforming system(VL=1,L=2,H=4and

VH=5),the quantitative chances of the five types of injury

occurring owing to cause A are light injury=1/(1+2+

2+4+5)=0.07;medium injury=2/(1+2+2+4+5)=0.14;

severe injury=2/(1+2+2+4+5)=0.14;disabling injury=

4/(1+2+2+4+5)=0.29;fatal injury=5/(1+2+2+4+

5)=0.36.These qualitative inputs can vary according to

cause and activity.

5.2.Calculating the factor and factor-condition

distributions

In SimSAFE,the simulation algorithm is run to

determine the likelihood of a particular cause occurring

based on the families of factor-condition distributions.

Deriving the mean(o i,j[k(c)])and standard deviation

(sd i,j[k(c)])of a family of factor-condition distributions

requires first determining the variance(SD2i(j,k))of the

factor distribution(f i(j,k)).Take cause A for activity X for

example.SD2i(j,k)is determined follows.(Table7lists the

calculated r2i(j)and SD2i(j,k)for each factor distribution). &Since the standard deviation(r i(j))of the overall distribution equals0.014853(see Table4),then

r2i(j)=0.014853?0.014853=0.0002206.

Table4

Three-point estimates of the likelihood of each cause(two-activity example)

Activity Cause Qualitative estimates of likelihood Transformed quantitative values of likelihood

Pessimistic estimate Most likely

estimate

Optimistic

estimate

Pessimistic

value

Most likely

value

Optimistic

value

Mean Standard

deviation

X A H SH SL0.071470.053600.023940.0516390.014853

E H SH SL0.002670.002000.000890.0019260.000554

B H SH SL0.032070.024050.010740.0231680.006664

L H SH SL0.002920.002190.000980.0021100.000607

D H SH SL0.071470.011710.005230.0205880.020701 Y A SH AS L0.080410.053600.026800.0536040.016751

E SH AS L0.003000.002000.001000.0019990.000625

B SH AS L0.036080.024050.012030.0240500.007516

L SH AS L0.003290.002190.001100.0021900.000684

D SH AS L0.017560.011710.005850.0117060.003658 H—higher;SH—slightly higher;AS—almost the same as;L—lower than.

Table5

Sensitivities of each factor to each cause(two-activity example)

Activity Cause Factor sensitivity

F1F2F3

X A High Low Medium

E High Medium Medium

B Medium Medium Medium

L Low Low High

D Medium Low Low

Y A Medium Low High

E High Medium Medium

B Medium Low High

L High Medium Medium

D Medium Low High Table6

Qualitative and quantitative estimates of the chance of each type of injury occurring(two-activity example)

Cause Chance

Light

injury

Medium

injury

Severe

injury

Disabling

injury

Death

A VL(0.07)L(0.14)L(0.14)H(0.29)VH(0.36) E VL(0.08)L(0.15)M(0.23)M(0.23)H(0.31)

B VL(0.08)VL(0.08)M(0.23)H(0.31)H(0.31) L VL(0.08)L(0.17)L(0.17)M(0.25)H(0.33) D L(0.13)M(0.19)L(0.13)H(0.25)VH(0.31) VL—very low;L—Low;H:high;VH:very high;M—medium.

The bracketed value represents the quantitative estimate of the chance of each type of injury arising.

W.-C.Wang et al./Automation in Construction15(2006)341–354349

&As indicated in Table 5,factors F1,F2and F3have high,low and medium sensitivities to cause A occurring for activity X ,respectively.The following values are assumed:w F 1[high]=8,w F 1[low]=1,and w F 1[me-dium]=5.Then,~K

k ?1W k Q i j ;k eT???8t1t5?14.&Based on Eq.(7a)(7b),Y i (j )=0.0002206?14=0.00001576.

&Based on Eq.(8),SD 2i (j ,F 1)=8?0.00001576=

0.00012606;SD 2

i (j ,F 2)=1?0.00001576=0.00001576;and SD 2i (j ,F 3)=5?0.00001576=0.00007879.Assume that the user chooses the categories better-than-expected,as-expected,and worse-than-expected to describe the conditions of the factor (F1).A family of three factor-condition distributions can then be constructed.Assume that the factor-condition distributions all have equal probabilities of occurrence.Restated,p 1=p 2=p 3=1/3.The following relationships thus can be identified based on Eqs.(9)and (10):

1=3eTo 1t1=3eTo 2t1=3eTo 3?0

e14T

1=3eTsd 21to 21áàt1=3eTsd 22to 22áàt1=3eTsd 23to 2

áà?0:00012606:e15TAssume ào 1=o 3=x and o 2=0so that Eq.(14)is satisfied.Let the factor-condition distributions have equal standard deviations.Eq.(15)then can be rewritten as sd 2

t2=3eTx 2

?0:00012606:

e16T

The limit of the value of x is determined by requiring the variance of the factor-condition distribution to be non-negative.That is,

sd 2?0:00012606à2=3eTx 20:

e17T

Thus,the limit in this case is x 0.013751(limit =0.013751).Namely,the values à0.013751and 0.013751are the two extreme means for factor-condition distributions one and three,respectively.The next step is to assign x a value between 0and 0.013751.Rather than specifying the exact value of x ,the SimSAFE model suggests selecting the value of x based on the level of factor sensitivity.For

example,o 3has the values 0.7Limit,0.5Limit,and 0.3Limit for high,medium,and low levels of influence,respectively.In this example (high level of influence),x is set to 0.7Limit =0.009626.Thus,the properties of the three factor-condition distributions are p 1=1/3,o 1=à0.00963,sd 1=0.00802;p 2=1/3,o 2=0,sd 2=0.00802;and p 3=1/3,o 3=0.00936,sd 3=0.00802.Table 8lists the properties of the factor-condition distribution for factor (F1)for the two activities (Chou provides further details [19]).

6.Example

To demonstrate the benefits of SimSAFE,this section applies SimSAFE to a high-tech facility construction project in northern Taiwan.The project includes a central utility building (CUB)and a fabrication building (FAB).The total floor area is 100,255m 2.The main CUB and FAB structures are made of reinforced concrete (RC)and steel reinforced concrete (SRC),respectively.Both buildings require foun-dations,structure,interior finishing and wall decoration.Fig.4illustrates the simplified schedule network,which includes 15activities (A1–A15),and the logical relation-ships among the various project activities.The figure also displays the five major working zones for this project,including zone 1(foundation for CUB;including A1–A4),zone 2(bottom RC structure for CUB;including A5and A6),zone 3(foundation for FAB;including A7and A8),zone 4(SRC for FAB;including A9–A11)and zone 5(finishing and decoration for both CUB and FAB;including A12–A15).Table 9also lists the duration of each activity and the number of workers involved.6.1.Inputs and evaluations

Table 10shows the three-point estimations of the likelihood for each cause in association with each activity.Based on Eqs.(2)and (3),Table 10also lists the values of M i (j )and r i (j )of the overall distribution due to each cause.Three factors are considered in the example,including

Table 7

Variance of factor distribution due to each cause (two-activity example)Activity Cause Variance (r i (j )2)Variance of factor distribution (SD i (j ,k )2)F1F2F3X

A 0.000220610.000126060.000015760.00007879E 0.000000310.000000140.000000090.00000009

B 0.000044410.000014800.000014800.00001480L 0.000000370.000000040.000000040.00000029D 0.000428540.000306100.000061220.00006122Y

A 0.000280610.000100220.000020040.00016035E 0.000000390.000000170.000000110.00000011

B 0.000056490.000020170.000004030.00003228L 0.000000470.000000210.000000130.00000013D

0.00001338

0.00000478

0.00000096

0.00000765

Table 8

Properties of factor-condition distribution due to factor F1(two-activity example)

Activity Cause Sensitivity Condition 1

Condition 2Condition 3p 1

sd 1o 1

o 2o 3X

A High 0.330.00802à0.009630.000000.00963E High 0.330.00026à0.000320.000000.00032

B Medium 0.330.00333à0.002360.000000.00236L Low 0.330.00018à0.000070.000000.00007D Medium 0.330.01515à0.010710.000000.01071Y

A Medium 0.330.00867à0.006130.000000.00613E High 0.330.00030à0.000360.000000.00036

B Medium 0.330.00389à0.002750.000000.00275L High 0.330.00033à0.000390.000000.00039D

Medium

0.33

0.00189

à0.00134

0.00000

0.00134

W.-C.Wang et al./Automation in Construction 15(2006)341–354

350

safety training (F1),site environment (F2),and subcon-tractor safety management ability (F3).(Again,the number of factors is not restricted in SimSAFE.)Table 11lists the sensitivity of each factor on the occurrence of each cause for each activity.Additionally,Table 12shows the qualitative estimates of the chances of various types of injury occurring due to each cause for each activity.6.2.Results

The inputs were processed and simulated for 1000iterations.Table 13presents the expected accident cost (H i )associated with each activity.The table also lists the mean occurrence of accident due to all causes for all factors

in association with each activity,and the standard deviation of accident occurrence due to all causes for a particular factor in association with each activity.Accordingly,the sensitivities of the likelihood of an accident’s occurrence to each factor can be calculated for each activity.Based on Eq.(13),Table 14displays the sensitivities of the likelihood of an accident’s occurrence to each factor,for each zone.6.2.1.Managing activities

The top five activities in the example project,to which most attention should be paid to prevent accidents are activities A14(H i =NT$115,046),A12($73,888),A5($69,758),A9($54,206)and A15($53,821).Therefore,whenever these activities are ready to be begun or are in progress,safety inspections must be performed frequently.Moreover,in controlling the safety of each activity,the factor that dominates the occurrence of accidents must be known.For instance,activity A14is most sensitive to F1,and then F3and F2.Hence,improving safety training (F1)is the most effective way to control the safety of A14.6.2.2.Managing paths

In the example project,the critical path contains activities A7,A8,A9,A10,A11,A12,A13and A15.As well as paying attention to controlling the duration of the critical activities,management should also note that on the critical path,F1most strongly affects the occurrence of accidents,followed by F3and F2.Effectively ensuring that the critical activities do not involve construction accidents ensures that work can be performed smoothly.Furthermore,in this example project,a near-critical path has 16days of float and comprises activities A7,A8,A9,A10,A11,A12and A14.This near-critical path is most sensitive to F3,followed by F1and F2.The expected accident cost on this near-critical path is $371,881—even higher than that on the critical path

($335,586).

Fig.4.Simplified schedule network for example project.

Table 9

Duration of,and number of workers involved in,each activity of the example project Activity number Description

Duration (days)Number of workers involved 1Excavation (CUB)

24202Anchored retaining walls (CUB)18153Reinforced-rebar piling (CUB)20154Steel piling (CUB)

1285Installing reinforced rebars and

forming (basement,CUB and FAB )45306Concreting (basement,CUB and FAB)29107Reinforced-rebar piling (FAB)23188Foundation

20249Steel erection (FAB)551510Deck erection (FAB)261511Concreting (FAB)

34812Installing reinforced rebars and forming (upper structure,CUB)1304213Concreting (upper structure,CUB)761614Interiors (CUB and FAB)1135515

Walls (CUB and FAB)

W.-C.Wang et al./Automation in Construction 15(2006)341–354351

6.2.3.Managing working zones

Construction safety sometimes is managed by working zones.In the example project,zone5has the greatest hazard (expected accident cost=$269,287),followed by zone4 ($145,374),zone1($103,398),zone3($70,911)and zone2 ($58,615).In managing zone5,activity A14has the most serious hazard.Additionally,the factor sensitivities in Table 14reveal the key zones in managing the performance of a particular factor.That is,F1is most sensitive to zone4;F2is most sensitive to zone1,and F3is most sensitive to zone4.

Table10

Three-point estimates,mean and standard deviation of the likelihood for each cause in association with each activity

Activity Cause Likelihood

Pessimistic Most

Likely Optimistic Mean Standard

deviation

1A H SL L0.0617040.033503

B AS SL L0.0223400.005010

F H AS SL0.0114410.004278

H SH AS SL0.0538770.013589 2A SH AS L0.0536040.016751

B AS SL L0.0167550.003758

E EH H SH0.0039980.000625

F H AS L0.0083620.003618 3A L SL EL0.0319840.001675

D SH AS L0.0117060.003658

F SH AS L0.0077190.002412 4D AS SL EL0.0042450.001171

F AS SL L0.0028680.000643 5A SL L EL0.0548550.009046

B H AS L0.0521090.022547

D AS SL EL0.0159200.004390

H H AS SL0.0873650.032664

L SH AS EL0.0043070.001506

O SL L EL0.0002290.000038 6E H AS L0.0014440.000625 N H AS L0.0000400.000017 7A L SL EL0.0383810.002010

D SH AS L0.0140470.004390

F SH AS L0.0092630.002895 8B H AS L0.0416870.018038

D AS SL L0.0130480.002927

E SL L EL0.0016370.000270

H SH SL L0.0490400.019647 9A H AS SL0.0595900.022279

D AS SL L0.0081550.001829

L SH SL L0.0017080.000684 10A H AS SL0.0595900.022279

D AS SL L0.0081550.001829

L SH AS L0.0021900.000684 11A H AS L0.0309710.013401

D AS SL EL0.0042450.001171 12A AS SL L0.1045640.023452

B SL L EL0.0344560.005682

D SH SL L0.0255660.010243

H AS SL L0.0766510.017191

L SH SL L0.0047830.001916

O H AS SL0.0006960.000260 13A SL L EL0.0292560.004824

E H AS SL0.0023700.000886

N SL L EL0.0000300.000005 14A SH AS L0.1965490.061421

H H SH SL0.2081950.059883

K SH AS L0.0030460.000952

L SH AS SL0.0082580.002083

O H SH AS0.0012300.000256 15A SH SL EL0.0818360.036853

D AS SL L0.0163100.003658 EH—extremely higher;H—higher;SH—slightly higher;AS—almost the same as;L—lower;EL—extremely lower.Table11

Sensitivity of each factor on the occurrence of each cause for each activity Activity Cause Sensitivity

Factor1Factor2Factor3 1A Low High Medium

B Low High Medium

F Medium High Low

H High Medium Low

2A Medium High Low

B Medium Medium Medium

E Low High Medium

F Low High Medium 3A Medium High Low

D High Low Medium

F High Low Medium 4D High Low Medium

F High Low Medium 5A High Low Medium

B Low High Medium

D High Low Medium

H High Low Medium

L Medium Low High

O High Low Medium 6E Medium Low High

N Medium No High

7A Medium High Low

D High Low Medium

F High Low Medium 8B Medium High Low

D High Low Medium

E High Low Medium

H Medium High Low

9A Medium Low High

D Medium Low High

L High Low Medium 10A High Low Medium

D High Low Medium

L Medium Low High 11A Low High Medium

D Medium Low High 12A High Low Medium

B Low High Medium

D High Low Medium

H High Low Medium

L Medium Low High

O High Low Medium 13A Low Medium High

E Medium Low High

N Medium No High 14A Medium Low High

H High Medium Low

K High Low Medium

L Medium Low High

O Medium Low High 15A Medium Low High

D Medium Low High

W.-C.Wang et al./Automation in Construction15(2006)341–354 352

6.2.4.Managing the project

The project is most sensitive to F1,followed by F3and F2.Effective safety training is most important for prevent-ing accidents in this example project.Additionally,the total

Table 12

Qualitative estimates of the chances of various types of injury occurring due to each cause for each activity Activity

Cause

Chance Light injury

Medium injury Severe injury Disabling injury Death

A H VH M L VL

B H VH L L VL F H VH L L VL H VH H L VL VL 2

A L VH H L VL

B M H L VL VL E L M H H L F VL M VH H L 3A VL L H M L D L M VH M VL F VL H VH M L 4D L VH H L VL F M H VH L VL 5

A M VH M L VL

B VH H M L VL D H VH M L VL H VH H M L VL L VL L H M VL O VL L VH H M 6E VL L VH H M N H VH M L VL 7

A VL L H M L D L M VH M VL F VL H VH M L 8

B H VH M L VL D M VH H L VL E M H VH L VL H H VH M L VL 9

A VL VL M H VH D VL L VH H M L M H VH L VL 10

A VL VL M H VH D VL L VH H M L M H VH L VL 11A VL VL M H VH D VL L VH H M 12

A M VH M L VL

B VH H M L VL D H VH M L VL H VH H M L VL L VL L H M VL O VL L VH H M 13

A M VH M L VL E VL L VH H M N H VH M L VL A M VH M L VL 14

H VH H M L VL K L M VH H L L VL L H M VL O VL L VH H M 15

A VL VL M H VH D

VH—very high;H—high;M—medium;L—low;VL—very low.

Table 13

Expected accident costs and sensitivities to factors for each activity Activity

Expected accident cost Factors

Accident occurrence Standard deviation (1)

Mean (2)CV (1)/(2)Rank A1

22,656

F10.0120.085

0.1413F20.0200.2351F30.0180.2122A231,490F10.0090.056

0.1612F20.0100.1791F30.0050.0893A328,853F10.0030.043

0.0701F20.0010.0233F30.0020.0472A41228F10.0020.005

0.4001F20.0000.0003F30.0010.2002A569,758F10.0190.136

0.1402F20.0150.1103F30.0210.1541A633F10.0010.010

0.1002F20.0000.0003F30.0020.2001A738,072F10.0040.051

0.0781F20.0020.0393F30.0030.0592A813,533F10.0140.059

0.2372F20.0150.2541F30.0070.1193A954,206F10.0110.041

0.2682F20.0060.1463F30.0120.2931A1043,288F10.0130.043

0.3021F20.0060.1403F30.0120.2792A1124,951F10.0030.018

0.1673F20.0080.4441F30.0070.3892A1273,888F10.0070.181

0.0393F20.0080.0442F30.0160.0881A1318,124F10.0010.025

0.0403F20.0020.0802F30.0030.1201A14115,046F10.0440.280

0.1571F20.0350.1253F30.0360.1292A1553,821F10.0180.053

0.3402F20.0090.1703F3

0.019

0.358

Table 14

Sensitivities of the likelihood of an accident’s occurrence to each factor for each zone Zones Sensitivity F1

F2F3Zone 10.0790.1160.095Zone 20.1360.1070.150Zone 30.1250.1250.071Zone 40.1650.1070.175Zone

0.095

0.069

0.086

W.-C.Wang et al./Automation in Construction 15(2006)341–354

353

expected cost of accidents for the entire project is$589,788, indicating the amount of contingency required for dealing with potential accidents.

7.Conclusions

This investigation presents an innovative simulation-based safety evaluation model(SimSAFE)that is integrated with the network schedule of a construction project. SimSAFE has two sources of theoretical strength.First, using qualitative model inputs is more practical than directly using quantitative inputs because practitioners are more familiar with qualitative estimates.Second,the uncertainty of an accident cause occurring is systematically disaggre-gated by safety factors and factor conditions according to a two-breakdown structure.This systematic structure eases the assessment of the influences of individual factors on accident occurrence.

Unlike earlier studies on integrating safety into planning, SimSAFE formally assesses the uncertainty in occurrence of causes of accidents(caught in and stuck by)and the hazards associated with each activity.It also explicitly combines safety information to schedule networks.Therefore,safety alerts can be proactively implemented at each time during the project.Moreover,the advantages of SimSAFE can be enhanced if it is applied to multiple projects with many activities.In that case,the efficiency of safety inspection will increase substantially,because a safety inspector or safety division(having a limited number of inspectors)can attend to highly sensitive activities,paths,zones and projects at any time.

Although the model inputs are designed to be qualitative (Tables10,11and12);the implementation of the model is found to be time-consuming.A user-friendly computer interface must be devised to simplify the use of SimSAFE in the future.Furthermore,additional historical data can be used to update the reference likelihood of each cause occurring(Table1),as well as updating the historical accident costs for each accident cause(Table3). Acknowledgements

The writers would like to thank the reviewers for their careful evaluation and thoughtful comments.The authors also thank the National Science Council of Taiwan for financially supporting this research under Contract No. NSC-90-2211-E-009-055.Professor S.C.Huang from the National Chiao Tung University is also appreciated for his valuable assistance.References

[1]E.J.Jaselskis,G.A.R.Suazo,A survey of construction site safety in

Honduras,Constr.Manag.Econ.12(1994)245–255.

[2]S.Salminen,Serious occupational accidents in the construction

industry,Constr.Manag.Econ.13(1995)299–306.

[3]C.M.Tam,I.W.H.Fung,Effectiveness of safety management

strategies on safety performance in Hong Kong,Constr.Manag.Econ.

16(1998)49–55.

[4]CLA,Occupational Accidents in1999¨2001(in Chinese),Council of

Labor Affairs,Executive Yuan,Taiwan,2002.

[5]T.A.Saurin,C.T.Formoso,L.B.M.Guimaraes,Safety and production:

an integrated planning and control model,Constr.Manag.Econ.22 (2004)159–169.

[6]N.A.Kartam,Integrating safety and health performance into

construction CPM,J.Constr.Eng.Manag.,ASCE123(2)(1997) 121–126.

[7]J.Hinze, C.Pederson,J.Fredley,Identifying root causes of

construction injuries,J.Constr.Eng.Manag.,ASCE124(1)(1998) 67–71.

[8]E.Sawacha,S.Naoum,D.Fong,Factors affecting safety performance

on construction sites,Int.J.Proj.Manag.17(5)(1999)309–315.

[9]D.P.Fang,F.Xie,X.Y.Huang,H.Li,Factors analysis-based studies

on construction workplace safety management in China,Int.J.Proj.

Manag.22(2004)43–49.

[10]https://www.wendangku.net/doc/259912797.html,ufer,W.B.Ledbetter,Assessment of safety performance

measures at construction sites,J.Constr.Eng.Manag.,ASCE112

(4)(1986)530–542.

[11]J.Hinze,D.C.Bren,N.Piepho,Experience modification rating as

measure of safety performance,J.Constr.Eng.Manag.,ASCE121(4) (1995)455–458.

[12]J.M.de la Garza, D.E.Hancher,L.Decker,Analysis of safety

indicators in construction,J.Constr.Eng.Manag.,ASCE124(4) (1998)312–314.

[13]A.R.Duff,I.T.Robertson,R.A.Phillips,M.D.Cooper,Improving

safety by the modification of behaviour,Constr.Manag.Econ.12 (1994)67–78.

[14]E.J.Jaselskis,S.D.Anderson,J.S.Russell,Strategies for achieving

excellence in construction safety performance,J.Constr.Eng.Manag., ASCE122(1)(1996)61–70.

[15]J.Hinze,L.L.Appelgate,Costs of construction injuries,J.Constr.

Eng.Manag.,ASCE117(3)(1991)537–550.

[16]J.G.Everett,P.B.Frank,Cost of accidents and injuries to the

construction industry,J.Constr.Eng.Manag.,ASCE122(2)(1996) 158–164.

[17]J.Gambatese,J.Hinze,Designing for construction worker safety,

Autom.Constr.8(1999)643–649.

[18]J.J.Moder,C.R.Philips,E.W.Davis,Project Management with CPM,

PERT and Precedence Diagramming,3rd edition,Van Nostrand Reinhold,New York,1983.

[19]S.C.Chou,Proactive Safety Management Model for Integrating

Construction accidents and Schedules,MS thesis(in Chinese), National Chiao Tung University,Taiwan,2002.

[20]W.C.Wang,L.A.Demsetz,Model for evaluating networks under

correlated uncertainty—NETCOR,J.Constr.Eng.Manag.,ASCE126

(6)(2000)458–466.

[21]IOSH,Investigation of Occupational Accident Cost for the Taiwan’s

Construction Industry(in Chinese),Research Report,Institute of Occupational Safety and Health(IOSH)(1995).

[22]J.C.Martinez,STROBOSCOPE:State and resource based simulation

of construction processes,PhD dissertation,University of Michigan, Ann Arbor,Mich.,1996.

W.-C.Wang et al./Automation in Construction15(2006)341–354 354

《工业机器人工程应用虚拟仿真》课程标准

《工业机器人工程应用虚拟仿真》课程标准制定人：高亮制定时间：2016年10月批准人：批准时间：适用专业：工业机器人技术专业课程类型：建议学时：102 学分：8 本课程旨在提高学生在机器人方面的综合素质，着重使学生掌握从事机器人加工类企业中机器人工作所必备的知识和基本技能，初步形成处理实际问题的能力。培养其分析问题和解决问题的学习能力，具备继续学习专业技术的能力；在本课程的学习中渗透思想道德和职业素养等方面的教育，使学生形成认真负责的工作态度和严谨的工作作风，为后续课程学习和职业生涯的发展奠定基础。一、课程分析（一）教学计划的制定和教学内容的选取根据培养应用技能型人才总目标，制订本专业教学计划，课程的教材配套，教学、实验、实训、课程设计大纲和指导书等教学文件齐全，近几年来引入了现代教学技术手段，已初步建设、形成了具有特色的全套课堂教学和实验教学课件。根据该课程的基本教学要求和特点，结合学时的安排，从教材的整体内容出发，有侧重地进行取舍，筛选出学生必须掌握的基本教学内容，较好地解决了教学中质量与数量的矛盾。通过本课程的学习，使学生了解工业机器人工程应用虚拟仿真的基础知识、机器人虚拟仿真的基本工作原理；掌握机器人工作站构建、RobotStudio中的建模功能、机器人离线轨迹编程、Smart组件的应用、带轨道或变位机的机器人系统创建于应用，以及RobotStudio的在线功能，具备使用RobotStudio仿真软件的能力和针对不同的机器人应用设计机器人方案的能力，为进一步学习其它机器人课程打下良好基础。（二）教学方法分析 1、本课程适宜采用理论、实践一体化的教学方法。坚持理论联系实际，突出实际上机训练，切实保证技能训练教学的时间和质量。

CATIA_V5_运动仿真分析1

第16章 CATIA 运动分析 16.1 曲轴连杆运动分析四缸发动机曲轴、连杆和活塞的运动分析是较复杂的机械运动。曲轴做旋转运动，连杆左做平动，活塞是直线往复运动。在用CATIA作曲轴、连杆和活塞的运动分析的步骤如下所示。（1）设置曲轴、连杆、活塞及活塞销的运动连接。（2）创建简易缸套机座。（3）设置曲轴与机座、活塞与活塞缸套之间的运动连接。（4）模拟仿真。（5）运动分析。 16.1.1 定义曲轴、连杆、活塞及活塞销的运动连接 1.新建组文件 (1)点击“开始”选取“机械设计”中的“装配件设计”模块，如图16-1所示。图16-1 进入“装配件设计”模块（2）进入装配件设计模块后，点击添加现有组件图标，再点击模型树上的Product1图标，此时会出现文件选择对话框，按住Ctrl键，分别选取“Chapter16/huo-sai-xiao.CATPart、huo-sai.CATPart 、lianganzujian.CATproduct、quzhou.CATpart”，将这些零件体载入到Product1中。（3）此时，零件体载入后重合到一起，点击分解图标，出现分解对话框如图16-2所示。然后点击模型树上的Product1,点击确定，此时弹出警告对话框，如图16-3所示，警告各零件的位置会发生变，点击警告对话框的按钮“是”，我们会发现各个零件分解开来。

图16-2 分解对话框图16-3 警告对话框（3）由于连杆体零件是装配体，各部分之间存在约束，点击“全部更新”按钮，我们会发现连杆体组件恢复装配后的样子。（4）点击“约束”工具栏中的“相合约束”图标，分别选择活塞销中心线及活塞孔中心线，如图16-4所示。然后点击“约束”工具栏中的“偏移约束”图标，选择活塞销的一个端面及活塞孔一侧的凹下去细环端面，如图16-5所示，此时出现“约束属性”对话框，如图16-6所示。将对话框中的“偏移”一栏改为“3.75mm”，点击“确定”按钮，完成活塞销端面和活塞内凹孔细环端面之间的偏移约束关系。点击“全部更新”按钮，完成活塞与活塞销之间的约束，如图16-7所示。自此完成添加零部件工作。

仿木施工工艺及技术要求

仿木施工工艺及技术要求 1、砼基面的仿木施工工艺及技术要求施工工艺流程砼桩体浇注成型➱凿毛➱表面清洗➱涂刷SPC界面剂➱ 抹装饰面层➱ 养护➱ 涂刷表面封闭漆。施工工艺和技术要求 1 ）砼桩体浇注成型按照设计要求成型混凝土桩体。 2）凿毛为了提高仿木装饰层与桩体的粘结性能，桩体拆模后进行凿毛处理。 3）表面清洗采用高压水混凝土桩体表面的粉尘、碎屑冲洗干净。 4）基层处理将混凝土桩体表面湿润，但不得有明水。按一定比例配制SPC界面剂，搅拌至无水泥颗粒、无沉淀。然后均匀涂刷，不漏涂，以保证仿木装饰层与桩体结构粘结良好。5）抹装饰层采用彩色SPC聚合物水泥砂浆进行装饰层的抹灰施工。所用的水泥、砂子、SPC乳液、水、色粉应符合有关技术要求，成都公司注册按照一定比例拌制而成，其抗冻等级为F150,面层厚度一般10?20mm。根据不同的仿木型式，装饰层分层抹面而成，外型依松树或榆树皮，并做树节、裂缝、脱落皮等仿真效果。对成型表面进行调整、补充、加固、艺术手法展现处理，达到仿真的效果。 6）仿木面层的养护主要为保水养护及冬季施工的保温措施。仿木面层完成后，采用塑料膜覆盖潮湿养护7 天，再自然养护7 天后进行表面处理。如果在冬季施工，应采取相应的保温防护措施，保证仿木面层在5 C以上养护环境。 7）表面处理对成型表面进行调整、补充、加固、艺术手法展现处理，搭配调和、调色、造光等处理。 8）防护涂层处理涂层材料采用环氧树脂对装饰面层表面进行喷涂处理，应严格控制施工工艺要求进行涂装施工，遇到雨天湿度大于80%或温度低于5 C时，禁止进行涂装施工。雨后须检查基层含水率合格后，方可进行施工。、质量保证措施质量方针以人才为根本，以法规为准则，阳台护栏不断提高员工素质，严格过程控制，持续改进艺术创作，为顾客提供满意的工程产品和优质服务。质量控制 1）仿木桩工程施工所需要的所有材料，主要包括水泥、砂子、SPC聚合物乳液、色粉、颜料、防冻剂均有厂家提供的产品合格证及材料技术指标试验报告单，均应达到设计要求。 2）严格按照彩色SPC聚合物砂浆配合比配制砂浆，计量精度为水泥土2%砂± 5%乳液及色粉控制在±1%以内。采用人工搅拌。砂浆应随拌随用，一般在1h 内用完。 3）养护：当仿木桩成品完成后在12h 内，采用塑料薄膜覆盖、洒水养护，保持塑料布内有凝结水，每天浇水的次数以桩体表面处于湿润状态来确定，养护时间不少于7 天，然后自然养护。 4）冬季施工措施：当温度低于5C时，应按比例掺加早强防冻剂。在每段仿木桩周围搭设保温棚，保证棚内温度不低于 5 C。

建筑施工工艺仿真心得体会

建筑施工工艺仿真心得体会篇一：仿真实习的学习心得及体会仿真实习的学习心得及体会（兰州理工大学技术工程学院化学工程与工艺09160207）经过连续两周的仿真实习，我们练习了离心泵、换热器、液位的控制、精馏塔的冷态开车、正常停车以及相应事故处理的仿真。通过这次仿真实习基本单元操作方法；增强了我对工艺过程的了解，进而也更加熟悉了控制系统的设计及操作。让我对离心泵、换热器、精馏塔等有了更深刻的了解和认识。通过本次的化工仿真实习收获颇多，对工艺流程、控制系统有了一定的了解，基本掌握了开车、停车等的规程。开始接触化工仿真软件时,感觉很迷漫也很好奇，在后来的实习过程中我首先仔细阅读了课本上实习的具体流程，基本明白了操作的规程。特别是在练习精馏塔单元等复杂的化工过程的时候，我觉得应该：（1）要仔细认真的阅读课本上相应的流程操作，对每一步操作都应该要有所领会、理解，因为过程的熟悉程度在操作中使至关重要的。过程不够熟悉也许会误入歧途，错误的操作，最后事倍功半，也不能很好的掌握所需学习的内容。

（2）面对一个复杂的工艺过程时，如果不能事先了解到它们的作用和相应的位置，以及各自开到什么程度，在开车时我们可能会手忙脚乱，导致错误的操作，因此，在开车前最重要的准备工作就是熟悉整个的工艺过程。（3）在开车后的操作中一定要有(转载于:https://www.wendangku.net/doc/259912797.html, 书业网:建筑施工工艺仿真心得体会)耐心，不能急于求成。无比达到每一步的工艺要求之后，才能进行下一步的操作，否则可能造成不可挽回的质量错误。因此在面对一个工艺流程，必须要了解这个工艺流程的作用是什么，要达到怎样的目的，了解流程中的各个环节，是如何进料的，操作条件又是如何，要达到什么样的要求。只有这样我们才能更好的学习或掌握所练习的学习内容。总之，通过二周的仿真实习，我明白了许多，同时也懂得了许多，在操作过程中对每一步工艺操作都要耐心的完成，要达到规定的要求，不能急于求成，否则会事倍功半。要不断的吸取失败的教训，虚心向老师和优秀的同学请教，总结经验。此外，在以后的学习和生活中，要更加刻苦、努力的学习自己的专业知识，夯实基础、扩大自己的知识面，从而在以后的工作或生活中，更好的为我所用，为以后踏上工作岗位打下基础! 篇二：施工标准化工艺学习心得施工标准化工艺学习心得

TFET工艺仿真

go athena line x loc=0.0 spac=0.001 line x loc=0.2 spac=0.001 line x loc=0.4 spac=0.001 line x loc=0.6 spac=0.001 line x loc=0.8 spac=0.001 line x loc=1.0 spac=0.001 line x loc=1.2 spac=0.001 line y loc=0.0 spac=0.005 line y loc=0.2 spac=0.008 line y loc=0.5 spac=0.03 line y loc=0.8 spac=0.1 # init orientation=100 c.boron=1e14 space.mul=2 #nwell formation including masking off of the pwell # diffus time=30 temp=1000 dryo2 press=1.00 hcl=3 # etch oxide thick=0.02 # #n-well Implant # implant phos dose=8e14 energy=100 pears # diffus temp=950 time=100 weto2 hcl=3 #

#p-well implant not shown - # # welldrive starts here diffus time=50 temp=1000 t.rate=4.000 dryo2 press=0.10 hcl=3 # diffus time=220 temp=1200 nitro press=1 # diffus time=90 temp=1200 t.rate=-4.444 nitro press=1 # etch oxide all # #sacrificial "cleaning" oxide diffus time=20 temp=1000 dryo2 press=1 hcl=3 # etch oxide all # #gate oxide grown here:- diffus time=11 temp=925 dryo2 press=1.00 hcl=3 # # Extract a design parameter extract name="gateox" thickness oxide mat.occno=1 x.val=0.05 # #vt adjust implant implant phos dose=9.5e11 energy=10 pearson # depo poly thick=0.2 divi=10 # #from now on the situation is 2-D

电火花加工蚀除过程动态仿真_解宝成

电火花加工蚀除过程动态仿真解宝成,王玉魁,王振龙 (哈尔滨工业大学微系统微结构教育部重点实验室,黑龙江哈尔滨150001) 摘要:在电火花单脉冲放电过程仿真的基础上,建立了电火花连续脉冲放电过程的三维热物理模型,利用有限元软件ANSYS模拟了连续脉冲放电过程的温度场分布,得到了连续脉冲放电过程不同时刻温度场分布和材料的动态蚀除过程,并根据脉冲放电的温度场和蚀除凹坑体积,分析了脉冲放电过程的残留温度分布和蚀除凹坑对后续脉冲放电过程的温度场分布和蚀除凹坑体积的影响规律。在仿真过程中,根据数值模拟极间电场强度和分析小孔加工初始时刻放电凹坑分布情况,确定了连续放电仿真模型放电位置的随机选择原则。并通过实验初步验证了材料仿真去除率,为预测材料去除率提供了理论基础。关键词:电火花加工;连续脉冲放电过程;温度场;材料去除率中图分类号:TG661文献标识码:A文章编号:1009-279X(2013)01-0006-05 Dynamic S imulation of Material Removal Process in EDM Process Xie Baocheng,Wang Yukui,Wang Zhenlong (H arbin Institute of Technolog y,H arbin150001,China) Abstract:In the paper,the three-dimensional thermal physical model of the continuous pulse dis-charge in EDM process has been developed on the base of the single pulse electrical discharge process simulation.Temperature field simulation and dy nam ic simulation of the material removal process has been carried out using ANSYS softw are and the previous pulse discharge process influence on the sub-sequent pulse discharge process is discussed in terms of temperature field distribution and the volume of craters.Finally,prediction of the material removal rate is also addressed,and its comparison w ith ex-perim ental measurements show s a good agreement,which provides a theoretical basis to predict m ater-i al removal rate in EDM process. Key words:EDM;continuous pulse spark discharge process;temperature field distribution;m aterial removal rate 由于电火花加工是利用极间火花放电产生大量的热使工件材料局部熔化、气化而被蚀除掉,以达到预定的加工要求,所以电火花加工技术具有非接触加工和无明显的宏观作用力等优点,在特殊及复杂形状的表面和零件以及难加工材料的加工上具有明显的优势,普遍应用于航空航天、模具加工、精密器械等领域。但电火花加工效率低和电极损耗是制约收稿日期:2012-10-09 基金项目:国家自然科学基金资助项目(51105111);国家重点基础研究发展计划(973计划)资助项目(2012CB934102) 第一作者简介:解宝成,男,1982年生,博士研究生。其发展应用的主要因素,由于其放电过程的随机性和复杂性,对材料蚀除过程进行研究显得尤其必要。电火花加工工件表面是由无数个放电凹坑叠加组成,因此单个放电凹坑的尺寸和分布决定了最终表面形貌和材料去除率,所以多是建立不同的单脉冲放电仿真模型,并进行大量的数值模拟和单脉冲放电实验来研究电火花加工过程的蚀除机理[1-6]。虽然电火花加工是连续无数个的单脉冲放电重复循环的过程,但与单脉冲放电过程有很大的区别,因此有必要研究连续脉冲放电过程的蚀除机理。许多学者对连续脉冲放电过程的材料去除率和表面质量也 ) 6 )

建筑工程施工工艺仿真心得体会

建筑工程施工工艺仿真心得体会篇一：建筑工地学习心得篇一：建筑工地实习心得体会实习心得体会 2013年4月1日至2013年4月31日，我在xxxxxx公司xxxx项目参加毕业实习，在代习老师的指导下，我完成了xxxx的施工任务。历时一个月的实习让自己突破了书本上的限制，真正的把理论和实际相辛苦的实习生活结束了，收获很多，现在将其总结如 1 筑，进一步提高我对建筑文化、建筑知识以及料的认识，巩固和扩大所学理论知识，提高学习积极 2 程及阅读施工图纸，进行现场比较，进一步培能力，提高识读工程图的能力。 3 建筑工程施工工艺，熟悉房屋构造，了解建筑 4

学生劳动的观点，发扬理论联系实际的作风，为技术工作奠定基础。容和亲身参加的具体工作过程：工员，由于实习的时间较有限，所以我学到的东准层的施工都亲身参加了工作。整个工作流程如下所作→焊接柱筋→绑扎柱箍筋→支模板架体→安装梁、梁钢筋→安装梁、板筋→浇筑柱、梁、板砼→砼的养板模。整个施工过程中还需包括水平和高程的放的施工学习之外，我还协助项目副经理进行施工进度结合起来。紧张而下：一、实习目的：、通过参观实际建建筑施工、建筑材性。、通过参观在建工养学生的空间想象、通过实习，了解材料的特性及应用。、通过实习,培养今后从事建筑工程二、实习的主要内我实习的岗位是施西也很有限，从标示：钢筋下料、制板、柱模板→焊接护→拆柱模→拆梁、样。除了对单幢楼的控制。整个混凝土结构工程包括了钢筋工程、模板工程、混凝土工程。但是也由于时间的仓促，整个实习过程我没有接触到屋面工程，和装修工程。以下将分别总结我在实习过程中所学习的知识以及我参加的工程：钢筋工程：

深基坑分段开挖施工过程仿真分析

深基坑分段开挖施工过程仿真分析随着城市化进程的加快,我国轨道交通工程的建设速度跃上了新台阶,随之带来的安全风险、进度风险和管理困难等问题也日益显著。在提倡安全文明施工的21世纪,地铁车站深基坑开挖过程需要适应周边及地下复杂的环境。为了深入了解地铁车站深基坑关键施工过程和潜在危险工况的力学性能,本文以武汉轨道交通阳逻线的黄埔新城站为例进行了以下研究:（1）为了研究分层分段开挖的建造过程,本文根据基坑主体结构施工方案,采用一次建模法,充分考虑基坑的几何尺寸、开挖面放坡、支护体系刚度、挖深、架撑顺序、土质参数和边界条件,利用有限元软件FLAC^3D对基坑8个施工阶段的内力和变形进行了数值分析,结果表明:所划分工况的基坑变形值没有超过设计范围,围护结构具有足够的安全储备,施工方案安全可靠。（2）通过监测数据与数值模拟结果进行对比分析,结果表明:大部分的监测数据和数值模拟结果的误差在可接受范围之内,模型精度符合预期。并分析了部分数据与监测值存在差异的原因。（3）对开挖过程中常见的钢支撑架设滞后险情进行了详细的分析,根据计算设置的几种滞后方案,得到了开挖面相应状态的变形值与轴力值,由此得出开挖面处最不利的缺撑形式,从而对施工现场钢支撑架设方案进行了优化。接着,又针对施工中容易出现的底部两排钢支撑架设滞后工况,提出了通过调整已架设钢支撑的预设轴力临时控制开挖面的变形的发展,并根据数值计算结果对措施效果进行了分析,结果表明:通过调节已架设钢支撑的轴力作为临时控制开挖面变形的临时措施是可行的,但变形值还会继续随着开挖面的暴露时长而增大。（4）建立了仅考虑分层开挖的数值模型,将结果与分层分段开挖模型进行

VERICUT虚拟加工仿真过程研究

VERICUT虚拟加工仿真过程研究随着现代工业的发展，零件的复杂程度、精度要求越来越高，经过软件自动生成的刀具路径处理后，生成的NC程序也更加复杂。因此，如何保证NC程序的精确性,成为数控加工生产中的一个难点。虚拟制造技术正是在这种背景下近年来出现的一种新的先进制造技术；在实际加工过程前，能够对具体加工过程进行仿真、优化,并对虚拟结果进行分析，可预先发现和改进实际加工中出现的问题，以较优的加工工艺投入生产。虚拟制造技术由建模技术、仿真技术、控制技术及支撑技术组成。其中，建模与仿真是虚拟制造技术的基础与核心。虚拟制造依靠建模与仿真技术模拟制造、生产和装配过程。虚拟加工环境是进行制造过程仿真、预测加工问题的前提和基础。本文将在虚拟制造软件VERICUT平台上，提出建立仿真机床的方法与过程，并结合具体实例，说明在VERICUT平台上进行虚拟机床建模的过程。 1 VERICUT主要功能 VERICUT是CGTech公司提供的一种专用于数控加工仿真的软件，具有较强的机床和NC程序的仿真功能。其主要功能模块如下： 1)Verification：三轴加工验证及分析。 2)OptiPath：对切削用量进行优化设计，以满足最小加工时间的目标函数及最大机床功率等约束条件的要求。 3)Model Export：从NC刀具路径创建CAD兼容模型。 4)Machine Simulation：提供虚拟机床及其工作环境建模功能；解读可识别的数控代码。 5)Mult-iAxis：四轴及五轴验证。 6)AUTO-DIFF：实时擦伤检查和模型分析，并与CAD设计模型相比较。 7)Machine Developerps Kit：定制VERICUT功能，用来解释复杂或不常用的数据。 8)AdvancedMachine Features：提高VERICUT仿真复杂机床功能的能力。 9)CAD/CAM Interfaces：可从Pro/E、UG、CA TIA等CAD/CAM系统内部无缝运行VERICUT。 10)VERICUT Utilities：模型修复工具和转换器(包括在验证模块中)。 2 虚拟机床的建模虚拟机床是随着虚拟制造技术的发展而提出的一个新的研究领域，通过虚拟机床加工系统可以优化加工工艺、预报和检测加工质量,同时还可以优化切削参数、刀具路径,提高机床设备的利用率和生产效率。在虚拟制造软件的研究领域中，建模的对象大多是局限于某一种或某一系列的机床，这种建模的方法不仅通用性差，工作量大，而且效率不高，影响仿真效果、制造周期和生产成本。针对不同类型机床的通用化建模方法是解决问题的必然出路，下面综合分析机床的结构特点，抽象出其功能模块，总结出通用性的建模方法。机床结构分析与模块分解：常见的数控机床在结构上主要有床身、立柱、运动轴和工作台等部件，再配合刀具、夹具和一些辅助部件共同组成。其中床身起到支承和承载机床组件的作用；立柱在结构上起到了拉开加工刀具和工件的空间距离，实现运动轴的布局；工作台则用来摆放工件，通过夹具等辅助工具实现工件的定位与夹紧。根据结构的特点可将机床的组件划分为三种类型：通用模块、辅助模块、专用模块。其中，通用模块是指各类机床共有的零/部件，如床身、立柱、工作台等等；辅助模块是指刀具、夹具等机床工具；专用模块

施工进度计划编制说明

施工进度计划编制说明 1.编制原则 ⑴项目控制性工期的要求； ⑵以本投标人工期目标和控制性工程节点为准则，以关键性工程的施工分期和施工程序为主导，协调安排各分项工程的施工进度选择最优方案； ⑶最大限度地组织均衡施工同时，做到实事求是，留有余地，保证工程质量和安全施工。当施工情况发生变化时，要及时调整和落实施工总进度； ⑷在施工过程中采取的施工工艺流程、施工方案、施工方法和保证措施； 2.施工总进度计划横道图另附 3.进度保证措施 3.1 组织保证措施本工程实行项目法施工管理，依据制定的《项目法施工管理实施细则》，落实工程进度计划的实施情况是对项目部考核的一项重要内容，并配套实施有关的进度计划目标保证措施和奖励政策。项目部各级主要管理负责人，按其职责划分，层层签订责任书，明确项目部各级人员的进度控制职责。加强日常管理和年度考核，充分调动全体干部、职工的积极性，从组织和管理制度上来确保工程进度按计划实施完成。（1）项目经理负责定期组织召开施工进度会议，按照施工进度对会议的组织设计和施工计划完成的实际情况进行监控，定期召开施工进度协调会。每旬召开一次旬施工进度协调会，每月召开一次月进度施工协调会，根据阶段性施工计划的完成情况召开阶段性施工进度会议。根据施工进度出现的特殊情况不定期召开施工进度专题会议，研究、部署施工进度专项计划措施。计划相关人员负责进度计划纠偏措施的编制，负责进度计划执行信息、资料的收集、整理。按照施工进度计划控制工作流程向主管人员提交上述文件，根据上级批示和指导意见对成果进行修正。记录和整理施工进度计划协调会议的内容，负责有关施工进度计划资料、文件的分发、存档、记录和上报。负责向本投标人主管部门上报本项目的施工进度完成情况和有关资料。（2）坚持项目部领导和技术人员现场施工值班制度，及时协调、处理、解决施工中出现的问题。项目经理、技术负责人和副经理每月驻守工地不少于21

虚拟仿真施工技术

1虚拟仿真施工技术（1）主要技术内容虚拟仿真施工技术是虚拟现实和仿真技术在工程施工领域应用的信息化技术。虚拟仿真技术在工程施工中的应用主要有以下几方面： A.施工工件动力学分析：如应力分析、强度分析； B.施工工件运动学仿真：如机构之间的连接与碰撞 C.施工场地优化布置：如外景仿真、建材堆放位置， D.施工机械的开行、安装过程； E.施工过程结构内力和变形变化过程跟踪分析； F.施工过程结构或构件及施工机械的运动学分析； G.施工过程动态演示和回放。 (2)技术指标虚拟仿真施工主要包含以下技术体系: A.三维建模技术运用三维建模和建筑信息模型（BIM）技术，建立用于进行虚拟施工和施工过程控制、成本控制的施工模型。该模型能将工艺参数与影响施工的属性联系起来，以反应施工模型与设计模型之间的交互作用，施工模型要具有可重用性，因此必须建立施工产品主模型描述框架，随着产品开发和施工过程的推进，模型描述日益详细。通过BIM技术，保持模型的一致性及模型信息的可继承性，实现虚拟施工过程各阶段和各方面的有效集成。 B.仿真技术计算机仿真是应用计算机对复杂的现实系统经过抽象和简化形成系统模型，

然后在分析的基础上运行此模型，从而得到系统一系列的统计性能。基本步骤为；研究系统→收集数据→建立系统模型→确定仿真算法→建立仿真模型→运行仿真模型→输出结果，包括数值仿真、可视化仿真和虚拟现实VR仿真。 C.优化技术优化技术将现实的物理模型经过仿真过程转化为数学模型以后，通过设定优化目标和运算方法，在制定的约束条件下，使目标函数达到最优，从而为决策者提供科学的、定量的依据。它使用的方法包括：线性规划、非线性规划、动态规划、运筹学、决策论和对策论等。 D.虚拟现实技术虚拟建造是在虚拟环境下实现的，虚拟现实技术是虚拟建造系统的核心技术。虚拟现实技术是一门融合了人工智能、计算机图形学、人机接口技术、多媒体工业建筑技术、网络技术、电子技术、机械技术等高新技术的综合信息技术。目的是利用计算机硬件、软件以及各种传感器创造出一个融合视觉、听觉、触觉甚至嗅觉，让人身临其境的虚拟环境。操作者沉浸其中并与之交互作用，通过多种媒体对感官的刺激，获得对所需解决问题的清晰和直观的认识。 (3)适用范围工业与民用建筑、市政工程、土木工程施工方案编制。

仿真草坪施工工艺

仿真草坪是人造草坪的一种，人造草皮以外观、性能与真草相似，耐磨，抗紫外线，抗老化，维护简便为人所称道。但由于其应用最为广泛的仍然是室外场地，长期日晒雨淋，且使用人次多、频率高，环境复杂多样，难免会对人造草皮造成一些影响。所以在使用中仍有一些事项需要注意。仿真草坪的施工工艺是怎样的？下面安徽华创人造草坪有限公司来为您解答！人造草皮维护原则一：保持人造草皮的清洁。一般情况下，空气中的各类粉尘不需要刻意清洗，自然雨水即可起到洗涤作用。但作为运动场地，这样理想的状态并不多见，因此需要及时清理草皮上的各种残渣，诸如果皮纸屑、瓜果饮料等等。轻巧型的垃圾可以用吸尘器解决，大一些的用毛刷清除，而污渍的处理则需使用对应成分的液剂，并迅速用水冲洗，但不要随意使用的清洁剂。人造草皮维护原则二：烟火会造成草皮损伤和安全隐患精品草坪

虽然现在的大多数人造草皮都具有阻燃功能，但很多人对人造草坪的阻燃性有一个误点，人造草坪的阻燃性不是烧不着，人造草坪还是属于塑胶材料是能烧着的，国标对人造草坪阻燃性的规定是点燃中心到损毁边缘的最大距离≤50mm，所以在场地上还是要避免烟头等明火的出现。人造草皮维护原则三：单位面积内的压强要控制。人造草皮场地上不要通行车辆，更不允许停车，也不能堆放物品。人造草皮固然有其自身的直立性和回弹性，但其负担的分量过重或时间过长，也会压坏草丝。人造草皮场地不能进行诸如标枪等需要使用尖锐体育器械的运动；足球比赛则不能穿长钉鞋，可用圆钉碎钉鞋代替；高跟鞋也不允许进场。人造草皮维护原则四：控制使用频率人造草皮虽然可以高频率的使用，但也不能无限制的持续承受高强度的运动。视使用情况而异，特别是激烈的运动过后，场地仍然需要一定的休息时间。在日常使用时遵循这些注意事项，一方面可以使人造草皮场地的运动功能保持更好的状态，另一方面也可以有效提高其使用寿命。另外在使用频率低的时候可以对场地进行整体检查，虽然大多数遇到的都是小损伤，及时修补才能防止问题扩大化。精品草坪

123施工工艺仿真教学软件产品介绍(1)

项目施工仿真教学软件产品介绍（VAS教学版）一、产品介绍施工工艺仿真教学软件作为一款以Unity3D技术为依托，综合行业规范、贯穿教学重难点，实现施工场景仿真模拟及工艺流程动态演示，人机交互式操作、成果实现智能考评等多项功能于一体的综合性专业仿真操作软件。对于现阶段院校建筑类专业课程授课过程中所存在的情景教学资源少、实训操作场地局限、实训操作道具成本较高、重复利用率低等情况，以及学生不同就业方向对技能的要求，软件采用了配套建筑信息化教学课程中的专业核心内容，进行了模块化虚拟操作体验，从而达到理论结合实践，实践贴近实际的效果。对于提高建筑行业整体水平有较高的指导性和先进性，意义重大。施工工艺仿真教学软件（VAS）是为各院校建筑类专业量身打造的实训操作体验平台。软件以案例场景作为大环境，实训操作任务内容以案例工程施工过程中单个节点为核心导向，结合施工技术、施工组织、工程质量检验、施工现场管理等几门课程作为理论基础，解决配套情景教学资源、实训操作场地、实训操作成本等问题，同步加强教师在实训过程中操作任务的考核，强化了教学手段。二、产品设计思路产品设计思路：系统目前整体以土建、装饰两个方向进行施工全过程、阶段性划分，结合行业规范和教学知识点的要求再将任务阶段合理化拆分成一系列流程性的任务，同时系统实现导训式、考核式两种版本操作模式，有助于学生了解实际施工过程中的完整流程，通过游戏式的人物场景漫游、任务操作来加强学生在实训过程中对于知识点的把控，同时也回顾了专业知识，强化了专业技能。

三、产品设计内容及理念施工工艺仿真教学软件（教学版） VAS软件按照“以理论为本位，以项目制教学为主线，以施工技术流程+专业专项教学体系”的总体设计要求，以项目任务为中心构建项目制教学管理体系。彻底打破常规学科课程的设计思路，紧紧围绕项目任务完成的需要来选择和组织教学内容，突出项目任务与知识的联系，让学生在实训时能够通过配套的视频体验更好的理解教学内容。案例工程三维场景 V-AS仿真操作系统是为院校建筑类专业量身打造的实训操作体验工具。软件内以案例场景作为大环境，实训操作任务内容以案例工程施工过程单个节点为核心导向，结合施工技术、施工组织、工程质量检验、施工现场管理等几门课程作为理论基础，解决配套情景教学资源、实训操作场地、实训操作成本等问题，同步加强了教师在实训过程中操作任务的考核，强化教学手段。人机交互式操作操作方式采用人机交互式体验，结合人体输入学设备可在PC客户端上进行任务操作体验，帮助学生了解掌握施工要点人物漫游式走动操作任务操作场景实现三维模拟，可漫游体验，场景配有可快位小地图功能，速定明确任务操作的位置，按实际工程施工阶段划分操作任务任务操作关键步骤可拆分，操作演示时可以随意选择每个步骤进行任务操作演示。系统设置任务操作工具栏软件下方设置了一系列的操作工具栏，学生可以快速通过添加或者删减工具栏中的工具来实现建筑施工中指定任务的模拟。

建筑工程施工中虚拟仿真技术的应用发展

建筑工程施工中虚拟仿真技术的应用发展摘要：随着社会经济的迅速发展，虚拟仿真技术在建筑工程施工中的运用，能够及时发现建筑工程施工中存在的不足，在提高施工质量的同时，还能从根本上节省建筑工程的成本投资，为其今后的投入使用奠定基础。在此，本文针对建筑工程施工中虚拟仿真技术的应用发展，做以下论述。关键词：建筑工程；施工；虚拟仿真技术；应用发展 Abstract: with the rapid development of social economy, the virtual simulation technology in architectural engineering in the construction of use, can prompt found construction, the deficiencies in the improvement of construction quality at the same time, still can fundamentally save the cost of construction project investment for the future of input use lay the foundation. In this, this article in view of the construction of the application of the virtual simulation technology development, do the following discusses. Keywords: building engineering; The construction; Virtual simulation technology; Application development 在21实际科学技术迅速发展的时代，计算机技术的普及，在推动社会发展的同时，还极大的改变了人们的日常生活。计算机智能系统在建筑工程施工中的运用，能够有效的规范工程施工技术，提高工程的管理水平，为建筑工程的施工质量提供有效的保障。在当前我国建筑工程施工领域中，常用的软件系统主要包括专家系统、智能管理系统、办公自动化系统等几个方面，这些计算机技术的应用，在原有的基础上弥补了传统施工方法及理论上的不足，避免了工程施工中不必要的麻烦。在此，本文从虚拟仿真技术、虚拟仿真系统在建筑施工中的应用以及虚拟仿真技术在工程施工中的研究和应用展望等三个方面出发，针对虚拟仿真技术在工程施工中存在的问题及完善途径，做以下简要分析：一．虚拟仿真技术在当前工程施工建设中，虚拟仿真技术是指通过计算机图形技术、计算机仿真技术、传感器技术、显示技术等多种学科的优势，为人机对话提供了更直接和更真实的三维界面，并能在多维信息空间上创建一个虚拟信息环境，使用户身临其境。在虚拟技术使用的过程中，除了能够对制定技术进行虚拟模仿外，还能通过真实逼真的环境，为技术的实提供相应的环境支持，在当前社会发展的过程中，虚拟仿真技术在多个领域中得到了飞速发展。而虚拟仿真技术在建筑工程施

施工仿真 (257)

《工业机器人工程应用虚拟仿真》课程标准

CATIA_V5_运动仿真分析1

最新施工进度计划

仿木施工工艺及技术要求

最新施工进度计划及工期保证措施

建筑施工工艺仿真心得体会

TFET工艺仿真

电火花加工蚀除过程动态仿真_解宝成

建筑工程施工工艺仿真心得体会

最新施工进度计划及进度保证措施资料

深基坑分段开挖施工过程仿真分析

VERICUT虚拟加工仿真过程研究

施工进度计划编制说明

虚拟仿真施工技术

仿真草坪施工工艺

123施工工艺仿真教学软件产品介绍(1)

最新施工进度计划和各阶段进度计划的保证措施

建筑工程施工中虚拟仿真技术的应用发展