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A hybrid simulation approach for integrating safety behavior into construction planning: An earthmoving case study
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A hybrid simulation approach for integrating safety behavior into construction planning: An earthmoving case study Yang Miang Goh a,∗ , Mohamed Jawad Askar Ali b a Safety and Resilience Research Unit (SaRRU), Department of Building, School of Design and Environment, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore b Safety and Resilience Research Unit (SaRRU), Department of Building, School of Design and Environment, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore
a r t i c l e
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Article history: Received 22 April 2015 Received in revised form 18 September 2015 Accepted 23 September 2015 Available online xxx Keywords: Hybrid simulation Construction safety Safety behavior Activity planning Simulation methodology
a b s t r a c t One of the key challenges in improving construction safety and health is the management of safety behavior. From a system point of view, workers work unsafely due to system level issues such as poor safety culture, excessive production pressure, inadequate allocation of resources and time and lack of training. These systemic issues should be eradicated or minimized during planning. However, there is a lack of detailed planning tools to help managers assess the impact of their upstream decisions on worker safety behavior. Even though simulation had been used in construction planning, the review conducted in this study showed that construction safety management research had not been exploiting the potential of simulation techniques. Thus, a hybrid simulation framework is proposed to facilitate integration of safety management considerations into construction activity simulation. The hybrid framework consists of discrete event simulation (DES) as the core, but heterogeneous, interactive and intelligent (able to make decisions) agents replace traditional entities and resources. In addition, some of the cognitive processes and physiological aspects of agents are captured using system dynamics (SD) approach. The combination of DES, agent-based simulation (ABS) and SD allows a more “natural” representation of the complex dynamics in construction activities. The proposed hybrid framework was demonstrated using a hypothetical case study. In addition, due to the lack of application of factorial experiment approach in safety management simulation, the case study demonstrated sensitivity analysis and factorial experiment to guide future research. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction The Workplace Safety and Health Institute (2015) in Singapore highlighted that the construction industry contributed to 57% of all fatal injuries in the first half of 2014. This is despite a decrease in total number of fatalities in all sectors. A total of 311,623 man-days were lost during the first half of 2014 due to workplace injuries in Singapore. Similar alarming trends could be seen throughout the world, including the United States (Zhang et al., 2015), Hong Kong (Li et al., 2015), Taiwan (Cheng et al., 2010) and Kuwait (Kartam and Bouz, 1998). Hence there is heightened interest to improve construction safety for both humanitarian as well as economic reasons. An important aspect of construction safety management is the quality and depth of safety consideration during construction
∗ Corresponding author. E-mail address: [email protected] (Y.M. Goh).
planning. Computer simulation is an established method for analysis and planning of construction operations and processes (Martinez, 2010). It had been applied in a wide range of construction contexts, e.g. planning for material laydown yards (Alanjari et al., 2014), floating caisson fabrication (Pantouvakis and Panas, 2013), bored piling (Zayed and Halpin, 2001), earthmoving (Marzouk and Moselhi, 2004) and bridge construction (Said et al., 2009). The range of problems that construction simulation models were meant to resolve is very wide, and some of the typical output variables evaluated include completion time, cost, productivity, number of resources deployed, and resource utilization. However, safety behavior considerations such as number of safety violations and diffusion of safety behavior are usually not considered in construction simulation. This is despite the fact that accidents is a perennial problem in the construction industry (Zhou et al., 2014) and safety behavior of workers is an important direct cause of construction accidents (Zhang and Fang, 2013). Safety behavior is also an important indicator of safety culture, which is fundamental to safety performance of organizations (Choudhry et al., 2007). Even
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though some of the current construction simulation studies do take safety into consideration, they are typically limited to basic constraints like space limitations (Marzouk and Ali, 2013) and working hours restrictions (Alvanchi et al., 2012). Thus, this study presents a hybrid simulation framework to integrate safety behavior considerations into construction simulation models. The proposed framework utilizes a combination of discrete event simulation (DES), agent-based simulation (ABS) and system dynamics (SD) to represent the different components of a construction activity. In general, DES is used to represent work processes, ABS is used to model individual agents (e.g. workers and machines), and SD is used to represent complex variables in agents. Such approach will facilitate a more balanced view of construction activities, where safety considerations will be considered earlier. A detailed case study is included to demonstrate the proposed framework. In view of the lack of application of simulation techniques in construction safety management research, the simulation methodology is presented in detail to guide future research in this area.
2. Simulation approaches Even though the range of simulation techniques is very wide, there are three main approaches: SD, ABS and DES (Pidd, 2004; Carley, 2009). SD is grounded in systems of differential equations and a SD model is made up of stocks, flows, and auxiliary variables that are inter-connected (Sterman, 2000). SD is known for its emphasis on feedback between variables and the delay between cause and effect. The core of a SD model is the stocks, which vary at each time step based on the difference between the flow rates in and out of the stock. A mathematical equation or an if–then rule is embedded within each variable or flow rate in the model and the values are analyzed using numerical methods. Variables are usually continuous and aggregated, where individual entities cannot be identified. However, the SD approach can be tweaked to track individual entities within the model. The modified approach is known as agent-oriented SD (e.g. Feola et al., 2012). In comparison to SD, ABS is focused on the design of individual agents and the adaptive decisions and actions that they perform. The ABS approach is also known as the “bottom-up” approach (Miller and Page, 2007), which contrasts with the “top-down” approach of SD (stipulating high level equations to represent different parts of a system). In ABS, agents can be heterogeneous and they have the ability to adapt and interact with each other and its environment in an autonomous fashion. Agents follow certain sets of rules and system behavior emerge from the interactions among the agents. Unlike SD models, DES models advance time from one event to another, rather than continuously. Each event corresponds to some significant change in the model and a queue of events is maintained in the model. Even though DES can be modeled in different ways, most DES models take a process view of the world, i.e. the core of the model is a sequence of steps or a flow chart, e.g. in a production line. Entities and resources such as material, equipment and people flow in the processes of a DES. By default, entities and resources are not able to interact with each other and they do not display adaptive behaviors as in ABS. DES is the most common form of simulation in construction research (Martinez, 2010). Hybrid simulation refers to a combination of two or more simulation approaches in a model. Even though it is possible to model most real life systems using one of the abovementioned simulation approaches, increasing level of complexity will often require significant improvisation of the selected simulation approach (Swinerd and Mcnaught, 2012). When dealing with multi-faceted systems, it may be advantageous to integrate two or more simulation
approaches so as to enable simple, natural and efficient representations. For instance, many DES would be more representative of the real world if entities are agents with the ability to adapt to changes in the model. It is also advantageous to integrate ABS with established SD decision making models by having the SD models embedded into the agents. 3. Simulation and safety management Safety management is the process of planning, implementing, checking and improving safety risk controls or interventions. Successful construction safety management hinges on detailed and early planning of construction activities. However, there is a lack of detailed planning tools to help managers assess the impact of the construction activities on safety behavior and performance. Due to its ubiquitous application in other areas of construction planning and evaluation, simulation appears to be a useful tool to facilitate safety planning of construction activities. Nevertheless, this study could not identify a comparable work that uses the full potential of simulation to identify safety interventions in construction management. As a sample of current safety management simulation studies, six papers were reviewed in detail: (1) Rudolph and Repenning (2002), (2) Cooke (2003), (3) Cooke and Rohleder (2006), (4) Salge and Milling (2006), (5) Sharpanskykh and Stroeve (2011) and (6) Feola et al. (2012). The first four papers were based on SD, the fifth is based on ABS and the last is based on agent-oriented SD. The SD models illustrated safety management theories based on major accidents. The agent-oriented SD model (Feola et al., 2012) and ABS (Sharpanskykh and Stroeve, 2011) were more practical and were focused on evaluation of safety interventions. A detailed review of the six papers can be found in (Goh and Palak, 2014). It was observed that SD was the most common approach and ABS is more suited for modeling safety behavior issues. It was noted that none of the six papers used factorial experimental design, which is an important analytical technique in simulation studies (Kelton and Barton, 2003). 4. Hybrid simulation framework Instead of using the simulation approaches individually, this study proposes a hybrid simulation framework to integrate safety behavioral considerations into construction activity planning. There are different ways to integrate DES, ABS and SD approaches. ˜ et al. (2008) used a SD–DES hybrid For example, Pena-Mora approach to model both operational and strategic levels in earthmoving activities. In their study, SD is used to model the strategic level, while DES is used to model the operational level. On the other hand, Swinerd and Mcnaught (2012) conducted a detailed review of ABS–SD models. Alvanchi et al. (2011) used a combination of SD and DES in modeling effect of working hours on construction activity. Some possible hybrid simulation frameworks adapted from Borshchev (2013) are summarized in Table 1. In this study, a DES–ABS–SD approach was selected. The proposed conceptual model framework is presented in Fig. 1. As highlighted earlier, even though it is always possible to stretch any of the simulation approach to cover all the desired features, the hybrid approach has the advantage of allowing complex problems to be represented more “naturally”, leading to improved efficiency and better communication with stakeholders of the simulation project. The framework consists of four quadrants, each highlighting a critical component of the framework. Since the proposed simulation is focused on operational concerns, it is useful to use DES as the core of the model. DES is widely accepted in the construction simulation literature as the default approach for modeling construction
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Table 1 Possible hybrid simulation framework for integrating safety behavior into construction activity planning. Hybrid approach
Description
ABS interact with SD
Humans, machineries and organizations are modeled in ABS and activities and project environment modeled using SD. The agents’ decisions are dependent on the variables in the SD simulation. The SD simulation can be based on established models such as the rework cycle Kun et al. (2011). Humans, machineries and organizations are modeled in ABS and activities and project environment modeled using DES. The agents’ decisions are dependent on the variables in the DES simulation. DES is very established in construction simulation and can be easily adapted to allow interaction with an ABS model. Humans, machineries and organizations are modeled in ABS. SD models are embedded in each agent to represent the decision making processes in the agents. The activities and environment are modeled using DES. At least some of the entities or resources in the DES process are modeled as agents.
ABS interact with DES
ABS with SD in agents DES with agents as entities and/or resources
Fig. 1. Conceptual framework for the proposed hybrid simulation model.
processes and activities (Abourizk, 2010; Martinez, 2010). As highlighted in the framework (Fig. 1), workflow or sequence of steps for production and safety will be captured using DES. Some examples of safety processes include application for safety permits, site inspections and accident investigation. ABS is used to model human and machine agents. Unlike the homogeneous entities and resources in traditional DES, agents in ABS are “naturally” heterogeneous, adaptive, have decision making capabilities and they interact with other agents. Thus, ABS is the most suitable for capturing the safety behavior of workers (human agents). The range of variables related to safety behaviors can be very wide; some examples are listed in the top left quadrant of Fig. 1, but as in the case for the other quadrants, the list is not meant to be comprehensive. The framework also suggests the use of SD to capture the internal dynamics of each human agents. SD has established equations and models to represent human decision making processes (Sterman, 2000) that can be utilized in the agents. In the proposed framework, the machine agents are essentially entities or resources in traditional DES models. However, when necessary, the entities or resources in DES can also be programmed to display agent characteristics, e.g. ability to interact and vary their behaviors. Lastly, the lower right quadrant highlights the physical and social environment that the processes, human agents and machine agents operate in. The environment defines the physical and social relationships among the processes and agents. It can also contain important environmental factors such as weather, temperature and humidity. 5. Case study The aim of this case study is to demonstrate how safety behavior considerations can be incorporated into construction planning through the proposed DES–ABS–SD framework. In addition, the
case study is also meant to guide future research on the use of simulation techniques in construction safety management. The case study was focused on earthmoving operation because it is a common construction operation that was frequently simulated ˜ (e.g. Smith et al., 1995; Pena-Mora et al., 2008; Vahdatikhaki and Hammad, 2014) and most readers should be able to relate to. Furthermore, truck drivers’ safety behavior is especially important to the safety performance of the earthmoving operation because the drivers have direct control over the trucks that they operate. In view of the focus on safety behavior and the need to keep the case study comprehensible, variables such as load pass time, number of passes per load, the different timings involved in the dumping different soil types, grade resistance and the rolling resistance of the road were not included. The case study describes the use of sensitivity analysis and factorial experimental design (Law, 2014) because it was observed that this is a neglected area in safety management simulation studies. The case study was implemented using the simulation software AnyLogicTM (Anylogic Company, 2014).
5.1. Case scenario In the hypothetical scenario, dump trucks and excavators are to be deployed to transport a stockpile of soil to a location to facilitate land reclamation work. However, the earthmoving contractor had a series of truck-related accidents and they are concerned about the safety behavior of the truck drivers. Thus, besides aiming to reduce completion time and minimize the number of plants deployed, the manager needs to consider ways to reduce the number of unsafe behaviors. Specifically, the contractor is concerned with speeding violations and driving under fatigue. The management levers include adjusting the number of trucks and excavators, increasing available rest time and conducting intensive safety training to change the safety attitude of drivers. Even though this case study is hypothetical, basic parameters such as loading duration, duration of breaks, capacity of trucks and speed limit were based on actual data from a cut and cover tunneling project. By default, the company allocated 10 trucks, 2 excavators, and 75 min of rest time per driver. Safety attitude of drivers is measured on a scale of 0–100 and is represented as a random variable with a triangular distribution. It is assumed that the manager consulted his supervisors and estimated the minimum, median and maximum safety attitude of his drivers as 20, 50 and 80 respectively. Safety climate surveys (e.g. Huang et al., 2013) and questionnaire developed based on behavioral models such as theory of planned behavior (e.g. Goh and Sa’adon, 2015) can be used to help managers estimate safety attitude more accurately. The default parameters for the case study are summarized in Table 2. It is assumed that the manager wants to find out how he can adjust parameters 1, 2, 7 and 8 in Table 2 to improve the job completion time, minimize resources deployed and reduce unsafe behaviors.
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4 Table 2 Default parameters for hypothetical case study. No.
Parameters
Values
1 2 3 4 5 6 7 8 9 10
Number of trucksa Number of excavatorsa Number of loading ports Volume of soil to be moved per day Truck capacity Haul distance Rest time per daya Median safety attitudea Speed limit Threshold attention level
10 2 2 2040 m3 18 m3 15 km 75 min 50 60 km/h 50
a
Adjustable parameter.
Start Queue
Return
No
Load
Haul
Rest?
Dump
Yes
Rest Fig. 2. Basic workflow for the earthmoving activity.
5.2. Processes – DES Fig. 2 shows the workflow for the earthmoving activity. The trucks queue to enter loading ports where excavator(s) is (are) used to load soil onto the truck. Once loaded, the trucks haul to the dumping point to unload the soil. Truck drivers can rest at the designated resting area in the dumping point whenever they want, but they are
allowed to rest for a maximum of 75 min per day. After unloading or resting, drivers will return to the queue for loading. Fig. 3 shows the discrete event simulation implementation of Fig. 2 in AnyLogic 7. The simulation ends when all the soil had been re-located and all the trucks had returned to their base location. 5.3. Truck and human agents – ABS–SD The trucks and the drivers are modeled as agents with the ability to alter their behaviors. Each driver starts off with a randomly assigned safety attitude level. Safety attitude affects the selection of speed during driving and it can be altered when drivers interact during rest breaks. Fig. 4 shows the state chart for modeling the behavior of truck drivers. Within the “working” state, the driver is driving and s/he can select the desired driving speed, but it will never go below 45 km/h or exceed 75 km/h. If the speed exceeds 60 km/h, a speeding violation is recorded in the simulation. The model keeps track of each driver’s fatigue level (measured by attention resource level) and the driver can rest if s/he has not exceeded the maximum rest time allocated. The driver can only rest at the dumping point and will have to drive while under fatigue if s/he runs out of rest time. The speed selection of the truck driver is modeled based on the simplified trip disutility concept model suggested by (Tarko, 2009), which states that the drivers trade-off a portion of their safety for their time gain. The safety attitude of the driver acts as a speed deterrent and the perceived value of time gain or the production pressure acts as speed enforcement. This relationship is in line with the theory by Fuller (2005), who indicated that drivers adjust their behavior to maintain the current workload below their capacity. The speed of the truck in the model depends on the SD Eqs. (1)–(3), Truck Speed = max(l, min(u, l + (Sf + (100 − Sa )) ∗ (Er − R)))
(1)
where u is the upper speed limit of the truck (75 km/h), l the lower speed limit of the truck (45 km/h), Sf is the default scaling factor
Fig. 3. Discrete event simulation for the earthmoving process.
Please cite this article in press as: Goh, Y.M., Askar Ali, M.J., A hybrid simulation approach for integrating safety behavior into construction planning: An earthmoving case study. Accid. Anal. Prev. (2015), http://dx.doi.org/10.1016/j.aap.2015.09.015
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5
= Min [Maximum Resource Recovery ∗(1 − Workload statust ), (100% − Attention Resource levelt ) /t + Resource Consumption Ratet ]
(5)
Attention Resource levelt
= Attention Resource levelt−t +(Resource Recovery Ratet−t Resource Consumption Ratet−t ) ∗ t
(6)
Once the Attention Resource levelt falls below the threshold attention level, the truck driver is deem to be in the tired state, i.e. under fatigue. The driver under fatigue will continue driving until s/he reaches the resting location (dump point) and only if he has not exceeded the maximum rest time allocated. 5.4. Social environment
Fig. 4. Truck driver state chart.
(80), Sa is the safety attitude of the driver (triangular (20, 50, 80)), R is the current rate of work and Er the expected rate of work. Eq. (1) captures the effect of production pressure on speed selection and how higher (lower) safety attitude can decrease (increase) the effect of production pressure. The rate of work, R, is calculated dynamically and at any given point of time, R is determined based on R=
total volume of soil moved time worked
(2) 5.5. Sensitivity analysis
Expected rate of work is given by N ∗ Tc Er = t ∗ Tn
(3)
where N is the expected total number of hauls, Tc is the capacity of the truck, t is the total time assigned for the earthmoving operation and Tn is the total number of trucks assigned. Fatigue is an important factor influencing safety performance. This study uses the SD model of mental fatigue by Alvanchi et al. (2012), which is based on the Theory of Limited Resources (Eqs. (4)–(6)) to model the mental fatigue of the truck driver during work. Resource Consumption ratet
= Min[(Resource Consumption Index −Maximum Resource Recovery Rate) ∗Workload Statust ,
When more than one driver is resting at the resting area, the drivers interact. It is assumed that whenever two drivers interact, they can potentially influence each other’s safety attitude. This process is based on the Theory of Planned Behavior (Ajzen, 1991), which indicates that intentional behavior, e.g. safety violation, is dependent on attitude, subjective norm and/or perceived behavioral control. In this case, the effect of subjective norm is assumed to be dominant. In the model, when two truck drivers meet, the driver with the higher absolute deviation from the median safety attitude is able to influence the other truck driver’s safety attitude. The affected truck driver will adopt a new level of safety attitude calculated based on the average of the safety attitude of the two drivers. As an illustration, two drivers (A and B) met at the rest point. Driver A’s safety attitude is 40 point higher than the median (50), and Driver B is 10 point lower than the median. In this case, Driver A is the “influencer” and Driver B’s new safety attitude is derived based on the average of the two drivers’ safety attitude ((90 + 40)/2 = 65). The final simulation model is an integration of the DES–SD–ABD model in which the operational flow of the earthmoving operation is defined by the DES system. The haul times, the resting decision and the co-worker influence of the truck drivers are determined by the underpinning ABS–SD models.
Attention Resource levelt t
+Resource Recovery Ratet ]
(4)
A simple one-factor-at-a-time (or one-way) sensitivity analysis (Chua et al., 1997; Goh and Chua, 2013) was conducted to demonstrate how to evaluate the need for more detailed data collection on selected parameters. In this case study, the sensitivity of the response variables to changes in the arbitrary constants such as median safety attitude, scaling factor (Eq. (1)), threshold attention level (see Table 2) and maximum recovery rate (Eq. (4)) were evaluated. The values of the parameters were increased by 10% of their base value individually. It is noted that the magnitude of the increase was arbitrarily chosen to assess the sensitiveness of the response variables to the factors or independent variables. This is acceptable as the sensitivity analysis is a preliminary analysis to guide further data collection. For each variation, the simulation model was executed 40 times and the percentage change in the average value of the response variables were captured in Table 3. Based on Table 3, the maximum recovery rate had the highest impact on the number of speeding violations and average time in tired status. Similarly, a 10% increase in the threshold attention
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6 Table 3 Sensitivity analysis of the assumed values. Response variable
Base value
% Change in response due to 10% increase in: Median safety attitude
Scaling factor
Threshold attention level
Maximum recovery rate
Number of speeding violations Average time in tired status (min) Job completion time (min)
4.3 83.3 635.0
−10.5% −0.2% 0
+38.5% −2.1% 0
+28% + 14.2% 0
−97.6% −35.2% 0
Table 4 Factor levels for the four-factor experiment. Factor No.
Factor
Negative level (−)
Positive level (+)
1 2 3 4
Number of trucks Number of excavators Resting duration (min) Median safety attitude
8 1 60 40
10 2 75 60
level leads to more than 10% change in number of speeding violations and average time in tired status. It is observed that only the number of speeding violations is sensitive to a 10% increase in the scaling factor. Job completion time is not sensitive to changes in the variables. However, maximum recovery rate, threshold attention level and scaling factor should be carefully evaluated because of their impact on number of speeding violations and average time in tired status. Some methods to increase the credibility of these variables are calibration based on available response data, comparing with expert opinion and comparing with established models (Law, 2014). The sensitivity analysis is a useful rough gauge of the impact of changes in parameters on the response variables. The results of the sensitivity analysis can provide guidance on whether more data should be collected to better represent an input parameter. However, it does not consider interactions between the different input parameters. 5.6. Factorial experimental design While the sensitivity analysis is useful for guiding further data collection, a more rigorous method for evaluating impact of changes in input variables is factorial experimental design (Montgomery and Runger, 1999). A factorial experiment is especially important when the possible combination of interventions to improve system performance is not clearly stipulated. The factorial experiment is meant to guide managers in identifying impactful
interventions and the interactions between factors (or controllable variables). Despite its importance, none of the six safety simulation papers reviewed (Rudolph and Repenning, 2002; Cooke, 2003; Cooke and Rohleder, 2006; Salge and Milling, 2006; Sharpanskykh and Stroeve, 2011; Feola et al., 2012) conducted factorial experiments. A 2k factorial design is performed herein to encourage its usage when utilizing the proposed framework. Table 4 shows the four key factors that the manager can modify and the two credible levels (negative and positive) identified by the manager. Since there are four factors and each can be varied based on the two levels, there are 16 possible combinations of the factors (see Table 5). For each of the 16 system configurations, the simulation was replicated 40 times and the results were analyzed in the statistical software SPSS. The average response values are captured in Table 5. The main effect of the factor j is the average change in the response due to moving the factor j from its “−” level to the “+” level while holding all other factors fixed. This average is taken over all the possible combination of factors. For example, the effect of factor 1, e1v , on the response variable “No. of speeding violations” is calculated as
e1v =
(V2 − V1 ) + (V4 − V3 ) + (V6 − V5 ) + · · · + (V16 − V15 ) 8
(7)
where V1 is the ‘Number of speeding violations’ corresponding to design point 1, V2 is the ‘Number of speeding violations’ corresponding to design point 2 and so on. Determining the interactions between the factors is one of the key advantages of conducting a factorial experiment over a sensitivity analysis (varying one variable at a time). Two factors interact when the effect of a factor is dependent on the value of another factor (Law, 2014). If interaction is ignored, effects can be erroneously assumed leading to ineffective interventions. As an example, the effect of the interaction between factor 1 and 2 on the response
Table 5 Experiment design matrix. Design point
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 a
Factora 1
2
3
4
− + − + − + − + − + − + − + − +
− − + + − − + + − − + + − − + +
− − − − + + + + − − − − + + + +
− − − − − − − − + + + + + + + +
No. of speeding violations
Average time in tired status (min)
Job completion time (min)
116.4 100.0 95.1 2.2 146.0 128.6 134.1 4.4 124.8 100.9 98.0 2.6 141.0 131.6 131.9 5.4
203.8 181.6 180.5 154.6 147.5 122.4 123.5 93.5 188.5 177.5 175.3 149.8 156.4 117.5 125.9 84.8
711.3 676.9 677.9 632.0 716.5 682.5 687.6 638.2 711.9 677.5 678.8 632.4 712.9 681.6 688.0 639.0
1 – number of trucks; 2 – number of excavators; 3 – rest duration; 4 – median safety attitude.
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Fig. 5. Main effects and factor interactions.
variable “No. of speeding violations” is calculated as e12v
1 = 2 −
(V − V ) + (V − V ) + (V − V ) + · · · + (V16 − V15 ) 7 4 3 8 12 11 4
(V2 − V1 ) + (V6 − V5 ) + (V10 − V9 ) + · · · + (V14 − V13 ) 4
(8)
Fig. 5 shows a summary of the results of the simulation experiments. As can be observed, even though many of the interactions are statistically significant, the actual magnitude of the interactions
diminish as the order of the interaction increases. Thus, the main effects dominate all the responses. 5.7. Implications Fig. 6 summarizes the results of the analysis. The manager is able to adjust the numbers of trucks and excavators, rest time and median safety attitude to balance activity completion time and safety performance. Safety performance is represented by the number of speeding violation and average time driven under fatigue, which are influenced by production pressure. Production pressure
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No of Speeding Violation
Expected production rate per driver -
Target completion time
+ Production + pressure + Completion time gap -
+
No of plants + No of trucks
Safety performance -
Median safety attitude +
Time driving under fatigue
Actual completion + time -
-
Available production time -
+ No of excavator
Rest time
Fig. 6. Influence diagram summarizing influence of factors on responses.
arise when the expected production rate per driver and/or when the gap between target completion time and actual completion time is increased. Actual completion time is decreased when the numbers of trucks and excavators are decreased. Although increasing rest time will reduce the average time driven under fatigue, it causes actual completion time to be increased. On the other hand, median safety attitude helps to reduce the effect of production pressure, but the effect is minor as compared to the effect of production pressure. Therefore, within the confines of the values stated in Table 4, increasing the numbers of trucks and excavators from 8 to 10 and 1 to 2 respectively, will help to reduce activity duration, and reduce production pressure on drivers. This indicates the importance of allocating sufficient plants for the activity. Increasing rest time and having intensive safety training targeted on improving safety attitude have effect on safety performance, but these effects can be easily negated by production pressure arising due to lack of resources.
In terms of analysis, the effects and interactions derived from the factorial experiments should be interpreted within the bounds of the factor levels. Future studies should utilize optimization techniques, such as genetic algorithms, to optimize the controllable factors automatically. Even though this study adopted AnyLogicTM to develop the case study, the conceptual framework and simulation approach can be implemented in any modeling tool or a general purpose programming language. Using a commercial software has many advantages in terms of having a more user-friendly development environment and technical support, but users can be restrained by the user interface and it also prevents collaborations among developers. The price of AnyLogicTM may also prevent researchers and user from adopting the proposed approach more willingly. Future research will explore how the hybrid simulation framework can be implemented in general purpose programming languages like Java or Python.
6. Limitations and future research
7. Conclusions
The study proposed a hybrid simulation framework for modeling construction activities so that safety behavior can be considered during planning. The case study showed how the hybrid simulation framework can be implemented. In view of the lack of application of factorial experimental approach in current safety management simulation studies, a detailed analysis was demonstrated. The case study was hypothetical and simple in many aspects, but this limitation does not affect the core purpose of this paper, which is to present the hybrid simulation framework for integrating safety behavior considerations into construction simulation models. Future research will implement the framework using actual data. One of the key challenges in implementing the framework is the modeling of the agents. This paper presented some suggested approaches, but future studies should review a wider range of fundamental psychological research on risk perception and safety behavior (e.g. Slovic, 2000, 2010) and select the appropriate model to represent safety behavior. In addition, more detailed calibration of assumed variables and comparison with experts and established models should be conducted to validate the simulation model. A “standard agent” can be created in the future to standardize the different behavioral, physiological and psychological aspects important to construction safety and productivity. With the “standard agent”, the complexity and effort for developing simulation models will be reduced significantly.
The construction industry needs to develop new approaches to improve its safety and health performance. One way to improve safety and health performance is to encourage early planning of construction processes and consider the possible impact on safety behavior. This study proposed a hybrid simulation framework to integrate safety behavior considerations in construction activity simulation. The hybrid simulation framework is based on a combination of discrete event simulation (DES), agent-based simulation (ABS) and system dynamics (SD). Since DES is the most common simulation approach in the construction industry, it is natural to use DES as the core of the proposed framework. However, in the proposed framework, the entities and resources in the DES are agents that are heterogeneous, able to make decisions and can interact with other agents. Due to the ability of SD to account for feedback and delays and its established approaches for modeling complex decision making, SD was used to model agents’ decision making and physiological processes. The DES–ABS–SD framework was demonstrated through a hypothetical earthmoving case study. A sensitivity analysis and a factorial experiment were conducted to encourage correct utilization of these analytical methods in construction safety management simulation. The case study showed how managers can utilize the hybrid simulation model to select suitable interventions to balance production and safety goals. In contrast to current construction planning simulation models, the proposed hybrid simulation approach allows integrates safety
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A hybrid simulation approach for integrating safety behavior into construction planning: An earthmoving case study Yang Miang Goh a,∗ , Mohamed Jawad Askar Ali b a Safety and Resilience Research Unit (SaRRU), Department of Building, School of Design and Environment, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore b Safety and Resilience Research Unit (SaRRU), Department of Building, School of Design and Environment, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore
a r t i c l e
i n f o
Article history: Received 22 April 2015 Received in revised form 18 September 2015 Accepted 23 September 2015 Available online xxx Keywords: Hybrid simulation Construction safety Safety behavior Activity planning Simulation methodology
a b s t r a c t One of the key challenges in improving construction safety and health is the management of safety behavior. From a system point of view, workers work unsafely due to system level issues such as poor safety culture, excessive production pressure, inadequate allocation of resources and time and lack of training. These systemic issues should be eradicated or minimized during planning. However, there is a lack of detailed planning tools to help managers assess the impact of their upstream decisions on worker safety behavior. Even though simulation had been used in construction planning, the review conducted in this study showed that construction safety management research had not been exploiting the potential of simulation techniques. Thus, a hybrid simulation framework is proposed to facilitate integration of safety management considerations into construction activity simulation. The hybrid framework consists of discrete event simulation (DES) as the core, but heterogeneous, interactive and intelligent (able to make decisions) agents replace traditional entities and resources. In addition, some of the cognitive processes and physiological aspects of agents are captured using system dynamics (SD) approach. The combination of DES, agent-based simulation (ABS) and SD allows a more “natural” representation of the complex dynamics in construction activities. The proposed hybrid framework was demonstrated using a hypothetical case study. In addition, due to the lack of application of factorial experiment approach in safety management simulation, the case study demonstrated sensitivity analysis and factorial experiment to guide future research. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction The Workplace Safety and Health Institute (2015) in Singapore highlighted that the construction industry contributed to 57% of all fatal injuries in the first half of 2014. This is despite a decrease in total number of fatalities in all sectors. A total of 311,623 man-days were lost during the first half of 2014 due to workplace injuries in Singapore. Similar alarming trends could be seen throughout the world, including the United States (Zhang et al., 2015), Hong Kong (Li et al., 2015), Taiwan (Cheng et al., 2010) and Kuwait (Kartam and Bouz, 1998). Hence there is heightened interest to improve construction safety for both humanitarian as well as economic reasons. An important aspect of construction safety management is the quality and depth of safety consideration during construction
∗ Corresponding author. E-mail address: [email protected] (Y.M. Goh).
planning. Computer simulation is an established method for analysis and planning of construction operations and processes (Martinez, 2010). It had been applied in a wide range of construction contexts, e.g. planning for material laydown yards (Alanjari et al., 2014), floating caisson fabrication (Pantouvakis and Panas, 2013), bored piling (Zayed and Halpin, 2001), earthmoving (Marzouk and Moselhi, 2004) and bridge construction (Said et al., 2009). The range of problems that construction simulation models were meant to resolve is very wide, and some of the typical output variables evaluated include completion time, cost, productivity, number of resources deployed, and resource utilization. However, safety behavior considerations such as number of safety violations and diffusion of safety behavior are usually not considered in construction simulation. This is despite the fact that accidents is a perennial problem in the construction industry (Zhou et al., 2014) and safety behavior of workers is an important direct cause of construction accidents (Zhang and Fang, 2013). Safety behavior is also an important indicator of safety culture, which is fundamental to safety performance of organizations (Choudhry et al., 2007). Even
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though some of the current construction simulation studies do take safety into consideration, they are typically limited to basic constraints like space limitations (Marzouk and Ali, 2013) and working hours restrictions (Alvanchi et al., 2012). Thus, this study presents a hybrid simulation framework to integrate safety behavior considerations into construction simulation models. The proposed framework utilizes a combination of discrete event simulation (DES), agent-based simulation (ABS) and system dynamics (SD) to represent the different components of a construction activity. In general, DES is used to represent work processes, ABS is used to model individual agents (e.g. workers and machines), and SD is used to represent complex variables in agents. Such approach will facilitate a more balanced view of construction activities, where safety considerations will be considered earlier. A detailed case study is included to demonstrate the proposed framework. In view of the lack of application of simulation techniques in construction safety management research, the simulation methodology is presented in detail to guide future research in this area.
2. Simulation approaches Even though the range of simulation techniques is very wide, there are three main approaches: SD, ABS and DES (Pidd, 2004; Carley, 2009). SD is grounded in systems of differential equations and a SD model is made up of stocks, flows, and auxiliary variables that are inter-connected (Sterman, 2000). SD is known for its emphasis on feedback between variables and the delay between cause and effect. The core of a SD model is the stocks, which vary at each time step based on the difference between the flow rates in and out of the stock. A mathematical equation or an if–then rule is embedded within each variable or flow rate in the model and the values are analyzed using numerical methods. Variables are usually continuous and aggregated, where individual entities cannot be identified. However, the SD approach can be tweaked to track individual entities within the model. The modified approach is known as agent-oriented SD (e.g. Feola et al., 2012). In comparison to SD, ABS is focused on the design of individual agents and the adaptive decisions and actions that they perform. The ABS approach is also known as the “bottom-up” approach (Miller and Page, 2007), which contrasts with the “top-down” approach of SD (stipulating high level equations to represent different parts of a system). In ABS, agents can be heterogeneous and they have the ability to adapt and interact with each other and its environment in an autonomous fashion. Agents follow certain sets of rules and system behavior emerge from the interactions among the agents. Unlike SD models, DES models advance time from one event to another, rather than continuously. Each event corresponds to some significant change in the model and a queue of events is maintained in the model. Even though DES can be modeled in different ways, most DES models take a process view of the world, i.e. the core of the model is a sequence of steps or a flow chart, e.g. in a production line. Entities and resources such as material, equipment and people flow in the processes of a DES. By default, entities and resources are not able to interact with each other and they do not display adaptive behaviors as in ABS. DES is the most common form of simulation in construction research (Martinez, 2010). Hybrid simulation refers to a combination of two or more simulation approaches in a model. Even though it is possible to model most real life systems using one of the abovementioned simulation approaches, increasing level of complexity will often require significant improvisation of the selected simulation approach (Swinerd and Mcnaught, 2012). When dealing with multi-faceted systems, it may be advantageous to integrate two or more simulation
approaches so as to enable simple, natural and efficient representations. For instance, many DES would be more representative of the real world if entities are agents with the ability to adapt to changes in the model. It is also advantageous to integrate ABS with established SD decision making models by having the SD models embedded into the agents. 3. Simulation and safety management Safety management is the process of planning, implementing, checking and improving safety risk controls or interventions. Successful construction safety management hinges on detailed and early planning of construction activities. However, there is a lack of detailed planning tools to help managers assess the impact of the construction activities on safety behavior and performance. Due to its ubiquitous application in other areas of construction planning and evaluation, simulation appears to be a useful tool to facilitate safety planning of construction activities. Nevertheless, this study could not identify a comparable work that uses the full potential of simulation to identify safety interventions in construction management. As a sample of current safety management simulation studies, six papers were reviewed in detail: (1) Rudolph and Repenning (2002), (2) Cooke (2003), (3) Cooke and Rohleder (2006), (4) Salge and Milling (2006), (5) Sharpanskykh and Stroeve (2011) and (6) Feola et al. (2012). The first four papers were based on SD, the fifth is based on ABS and the last is based on agent-oriented SD. The SD models illustrated safety management theories based on major accidents. The agent-oriented SD model (Feola et al., 2012) and ABS (Sharpanskykh and Stroeve, 2011) were more practical and were focused on evaluation of safety interventions. A detailed review of the six papers can be found in (Goh and Palak, 2014). It was observed that SD was the most common approach and ABS is more suited for modeling safety behavior issues. It was noted that none of the six papers used factorial experimental design, which is an important analytical technique in simulation studies (Kelton and Barton, 2003). 4. Hybrid simulation framework Instead of using the simulation approaches individually, this study proposes a hybrid simulation framework to integrate safety behavioral considerations into construction activity planning. There are different ways to integrate DES, ABS and SD approaches. ˜ et al. (2008) used a SD–DES hybrid For example, Pena-Mora approach to model both operational and strategic levels in earthmoving activities. In their study, SD is used to model the strategic level, while DES is used to model the operational level. On the other hand, Swinerd and Mcnaught (2012) conducted a detailed review of ABS–SD models. Alvanchi et al. (2011) used a combination of SD and DES in modeling effect of working hours on construction activity. Some possible hybrid simulation frameworks adapted from Borshchev (2013) are summarized in Table 1. In this study, a DES–ABS–SD approach was selected. The proposed conceptual model framework is presented in Fig. 1. As highlighted earlier, even though it is always possible to stretch any of the simulation approach to cover all the desired features, the hybrid approach has the advantage of allowing complex problems to be represented more “naturally”, leading to improved efficiency and better communication with stakeholders of the simulation project. The framework consists of four quadrants, each highlighting a critical component of the framework. Since the proposed simulation is focused on operational concerns, it is useful to use DES as the core of the model. DES is widely accepted in the construction simulation literature as the default approach for modeling construction
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Table 1 Possible hybrid simulation framework for integrating safety behavior into construction activity planning. Hybrid approach
Description
ABS interact with SD
Humans, machineries and organizations are modeled in ABS and activities and project environment modeled using SD. The agents’ decisions are dependent on the variables in the SD simulation. The SD simulation can be based on established models such as the rework cycle Kun et al. (2011). Humans, machineries and organizations are modeled in ABS and activities and project environment modeled using DES. The agents’ decisions are dependent on the variables in the DES simulation. DES is very established in construction simulation and can be easily adapted to allow interaction with an ABS model. Humans, machineries and organizations are modeled in ABS. SD models are embedded in each agent to represent the decision making processes in the agents. The activities and environment are modeled using DES. At least some of the entities or resources in the DES process are modeled as agents.
ABS interact with DES
ABS with SD in agents DES with agents as entities and/or resources
Fig. 1. Conceptual framework for the proposed hybrid simulation model.
processes and activities (Abourizk, 2010; Martinez, 2010). As highlighted in the framework (Fig. 1), workflow or sequence of steps for production and safety will be captured using DES. Some examples of safety processes include application for safety permits, site inspections and accident investigation. ABS is used to model human and machine agents. Unlike the homogeneous entities and resources in traditional DES, agents in ABS are “naturally” heterogeneous, adaptive, have decision making capabilities and they interact with other agents. Thus, ABS is the most suitable for capturing the safety behavior of workers (human agents). The range of variables related to safety behaviors can be very wide; some examples are listed in the top left quadrant of Fig. 1, but as in the case for the other quadrants, the list is not meant to be comprehensive. The framework also suggests the use of SD to capture the internal dynamics of each human agents. SD has established equations and models to represent human decision making processes (Sterman, 2000) that can be utilized in the agents. In the proposed framework, the machine agents are essentially entities or resources in traditional DES models. However, when necessary, the entities or resources in DES can also be programmed to display agent characteristics, e.g. ability to interact and vary their behaviors. Lastly, the lower right quadrant highlights the physical and social environment that the processes, human agents and machine agents operate in. The environment defines the physical and social relationships among the processes and agents. It can also contain important environmental factors such as weather, temperature and humidity. 5. Case study The aim of this case study is to demonstrate how safety behavior considerations can be incorporated into construction planning through the proposed DES–ABS–SD framework. In addition, the
case study is also meant to guide future research on the use of simulation techniques in construction safety management. The case study was focused on earthmoving operation because it is a common construction operation that was frequently simulated ˜ (e.g. Smith et al., 1995; Pena-Mora et al., 2008; Vahdatikhaki and Hammad, 2014) and most readers should be able to relate to. Furthermore, truck drivers’ safety behavior is especially important to the safety performance of the earthmoving operation because the drivers have direct control over the trucks that they operate. In view of the focus on safety behavior and the need to keep the case study comprehensible, variables such as load pass time, number of passes per load, the different timings involved in the dumping different soil types, grade resistance and the rolling resistance of the road were not included. The case study describes the use of sensitivity analysis and factorial experimental design (Law, 2014) because it was observed that this is a neglected area in safety management simulation studies. The case study was implemented using the simulation software AnyLogicTM (Anylogic Company, 2014).
5.1. Case scenario In the hypothetical scenario, dump trucks and excavators are to be deployed to transport a stockpile of soil to a location to facilitate land reclamation work. However, the earthmoving contractor had a series of truck-related accidents and they are concerned about the safety behavior of the truck drivers. Thus, besides aiming to reduce completion time and minimize the number of plants deployed, the manager needs to consider ways to reduce the number of unsafe behaviors. Specifically, the contractor is concerned with speeding violations and driving under fatigue. The management levers include adjusting the number of trucks and excavators, increasing available rest time and conducting intensive safety training to change the safety attitude of drivers. Even though this case study is hypothetical, basic parameters such as loading duration, duration of breaks, capacity of trucks and speed limit were based on actual data from a cut and cover tunneling project. By default, the company allocated 10 trucks, 2 excavators, and 75 min of rest time per driver. Safety attitude of drivers is measured on a scale of 0–100 and is represented as a random variable with a triangular distribution. It is assumed that the manager consulted his supervisors and estimated the minimum, median and maximum safety attitude of his drivers as 20, 50 and 80 respectively. Safety climate surveys (e.g. Huang et al., 2013) and questionnaire developed based on behavioral models such as theory of planned behavior (e.g. Goh and Sa’adon, 2015) can be used to help managers estimate safety attitude more accurately. The default parameters for the case study are summarized in Table 2. It is assumed that the manager wants to find out how he can adjust parameters 1, 2, 7 and 8 in Table 2 to improve the job completion time, minimize resources deployed and reduce unsafe behaviors.
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4 Table 2 Default parameters for hypothetical case study. No.
Parameters
Values
1 2 3 4 5 6 7 8 9 10
Number of trucksa Number of excavatorsa Number of loading ports Volume of soil to be moved per day Truck capacity Haul distance Rest time per daya Median safety attitudea Speed limit Threshold attention level
10 2 2 2040 m3 18 m3 15 km 75 min 50 60 km/h 50
a
Adjustable parameter.
Start Queue
Return
No
Load
Haul
Rest?
Dump
Yes
Rest Fig. 2. Basic workflow for the earthmoving activity.
5.2. Processes – DES Fig. 2 shows the workflow for the earthmoving activity. The trucks queue to enter loading ports where excavator(s) is (are) used to load soil onto the truck. Once loaded, the trucks haul to the dumping point to unload the soil. Truck drivers can rest at the designated resting area in the dumping point whenever they want, but they are
allowed to rest for a maximum of 75 min per day. After unloading or resting, drivers will return to the queue for loading. Fig. 3 shows the discrete event simulation implementation of Fig. 2 in AnyLogic 7. The simulation ends when all the soil had been re-located and all the trucks had returned to their base location. 5.3. Truck and human agents – ABS–SD The trucks and the drivers are modeled as agents with the ability to alter their behaviors. Each driver starts off with a randomly assigned safety attitude level. Safety attitude affects the selection of speed during driving and it can be altered when drivers interact during rest breaks. Fig. 4 shows the state chart for modeling the behavior of truck drivers. Within the “working” state, the driver is driving and s/he can select the desired driving speed, but it will never go below 45 km/h or exceed 75 km/h. If the speed exceeds 60 km/h, a speeding violation is recorded in the simulation. The model keeps track of each driver’s fatigue level (measured by attention resource level) and the driver can rest if s/he has not exceeded the maximum rest time allocated. The driver can only rest at the dumping point and will have to drive while under fatigue if s/he runs out of rest time. The speed selection of the truck driver is modeled based on the simplified trip disutility concept model suggested by (Tarko, 2009), which states that the drivers trade-off a portion of their safety for their time gain. The safety attitude of the driver acts as a speed deterrent and the perceived value of time gain or the production pressure acts as speed enforcement. This relationship is in line with the theory by Fuller (2005), who indicated that drivers adjust their behavior to maintain the current workload below their capacity. The speed of the truck in the model depends on the SD Eqs. (1)–(3), Truck Speed = max(l, min(u, l + (Sf + (100 − Sa )) ∗ (Er − R)))
(1)
where u is the upper speed limit of the truck (75 km/h), l the lower speed limit of the truck (45 km/h), Sf is the default scaling factor
Fig. 3. Discrete event simulation for the earthmoving process.
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5
= Min [Maximum Resource Recovery ∗(1 − Workload statust ), (100% − Attention Resource levelt ) /t + Resource Consumption Ratet ]
(5)
Attention Resource levelt
= Attention Resource levelt−t +(Resource Recovery Ratet−t Resource Consumption Ratet−t ) ∗ t
(6)
Once the Attention Resource levelt falls below the threshold attention level, the truck driver is deem to be in the tired state, i.e. under fatigue. The driver under fatigue will continue driving until s/he reaches the resting location (dump point) and only if he has not exceeded the maximum rest time allocated. 5.4. Social environment
Fig. 4. Truck driver state chart.
(80), Sa is the safety attitude of the driver (triangular (20, 50, 80)), R is the current rate of work and Er the expected rate of work. Eq. (1) captures the effect of production pressure on speed selection and how higher (lower) safety attitude can decrease (increase) the effect of production pressure. The rate of work, R, is calculated dynamically and at any given point of time, R is determined based on R=
total volume of soil moved time worked
(2) 5.5. Sensitivity analysis
Expected rate of work is given by N ∗ Tc Er = t ∗ Tn
(3)
where N is the expected total number of hauls, Tc is the capacity of the truck, t is the total time assigned for the earthmoving operation and Tn is the total number of trucks assigned. Fatigue is an important factor influencing safety performance. This study uses the SD model of mental fatigue by Alvanchi et al. (2012), which is based on the Theory of Limited Resources (Eqs. (4)–(6)) to model the mental fatigue of the truck driver during work. Resource Consumption ratet
= Min[(Resource Consumption Index −Maximum Resource Recovery Rate) ∗Workload Statust ,
When more than one driver is resting at the resting area, the drivers interact. It is assumed that whenever two drivers interact, they can potentially influence each other’s safety attitude. This process is based on the Theory of Planned Behavior (Ajzen, 1991), which indicates that intentional behavior, e.g. safety violation, is dependent on attitude, subjective norm and/or perceived behavioral control. In this case, the effect of subjective norm is assumed to be dominant. In the model, when two truck drivers meet, the driver with the higher absolute deviation from the median safety attitude is able to influence the other truck driver’s safety attitude. The affected truck driver will adopt a new level of safety attitude calculated based on the average of the safety attitude of the two drivers. As an illustration, two drivers (A and B) met at the rest point. Driver A’s safety attitude is 40 point higher than the median (50), and Driver B is 10 point lower than the median. In this case, Driver A is the “influencer” and Driver B’s new safety attitude is derived based on the average of the two drivers’ safety attitude ((90 + 40)/2 = 65). The final simulation model is an integration of the DES–SD–ABD model in which the operational flow of the earthmoving operation is defined by the DES system. The haul times, the resting decision and the co-worker influence of the truck drivers are determined by the underpinning ABS–SD models.
Attention Resource levelt t
+Resource Recovery Ratet ]
(4)
A simple one-factor-at-a-time (or one-way) sensitivity analysis (Chua et al., 1997; Goh and Chua, 2013) was conducted to demonstrate how to evaluate the need for more detailed data collection on selected parameters. In this case study, the sensitivity of the response variables to changes in the arbitrary constants such as median safety attitude, scaling factor (Eq. (1)), threshold attention level (see Table 2) and maximum recovery rate (Eq. (4)) were evaluated. The values of the parameters were increased by 10% of their base value individually. It is noted that the magnitude of the increase was arbitrarily chosen to assess the sensitiveness of the response variables to the factors or independent variables. This is acceptable as the sensitivity analysis is a preliminary analysis to guide further data collection. For each variation, the simulation model was executed 40 times and the percentage change in the average value of the response variables were captured in Table 3. Based on Table 3, the maximum recovery rate had the highest impact on the number of speeding violations and average time in tired status. Similarly, a 10% increase in the threshold attention
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6 Table 3 Sensitivity analysis of the assumed values. Response variable
Base value
% Change in response due to 10% increase in: Median safety attitude
Scaling factor
Threshold attention level
Maximum recovery rate
Number of speeding violations Average time in tired status (min) Job completion time (min)
4.3 83.3 635.0
−10.5% −0.2% 0
+38.5% −2.1% 0
+28% + 14.2% 0
−97.6% −35.2% 0
Table 4 Factor levels for the four-factor experiment. Factor No.
Factor
Negative level (−)
Positive level (+)
1 2 3 4
Number of trucks Number of excavators Resting duration (min) Median safety attitude
8 1 60 40
10 2 75 60
level leads to more than 10% change in number of speeding violations and average time in tired status. It is observed that only the number of speeding violations is sensitive to a 10% increase in the scaling factor. Job completion time is not sensitive to changes in the variables. However, maximum recovery rate, threshold attention level and scaling factor should be carefully evaluated because of their impact on number of speeding violations and average time in tired status. Some methods to increase the credibility of these variables are calibration based on available response data, comparing with expert opinion and comparing with established models (Law, 2014). The sensitivity analysis is a useful rough gauge of the impact of changes in parameters on the response variables. The results of the sensitivity analysis can provide guidance on whether more data should be collected to better represent an input parameter. However, it does not consider interactions between the different input parameters. 5.6. Factorial experimental design While the sensitivity analysis is useful for guiding further data collection, a more rigorous method for evaluating impact of changes in input variables is factorial experimental design (Montgomery and Runger, 1999). A factorial experiment is especially important when the possible combination of interventions to improve system performance is not clearly stipulated. The factorial experiment is meant to guide managers in identifying impactful
interventions and the interactions between factors (or controllable variables). Despite its importance, none of the six safety simulation papers reviewed (Rudolph and Repenning, 2002; Cooke, 2003; Cooke and Rohleder, 2006; Salge and Milling, 2006; Sharpanskykh and Stroeve, 2011; Feola et al., 2012) conducted factorial experiments. A 2k factorial design is performed herein to encourage its usage when utilizing the proposed framework. Table 4 shows the four key factors that the manager can modify and the two credible levels (negative and positive) identified by the manager. Since there are four factors and each can be varied based on the two levels, there are 16 possible combinations of the factors (see Table 5). For each of the 16 system configurations, the simulation was replicated 40 times and the results were analyzed in the statistical software SPSS. The average response values are captured in Table 5. The main effect of the factor j is the average change in the response due to moving the factor j from its “−” level to the “+” level while holding all other factors fixed. This average is taken over all the possible combination of factors. For example, the effect of factor 1, e1v , on the response variable “No. of speeding violations” is calculated as
e1v =
(V2 − V1 ) + (V4 − V3 ) + (V6 − V5 ) + · · · + (V16 − V15 ) 8
(7)
where V1 is the ‘Number of speeding violations’ corresponding to design point 1, V2 is the ‘Number of speeding violations’ corresponding to design point 2 and so on. Determining the interactions between the factors is one of the key advantages of conducting a factorial experiment over a sensitivity analysis (varying one variable at a time). Two factors interact when the effect of a factor is dependent on the value of another factor (Law, 2014). If interaction is ignored, effects can be erroneously assumed leading to ineffective interventions. As an example, the effect of the interaction between factor 1 and 2 on the response
Table 5 Experiment design matrix. Design point
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 a
Factora 1
2
3
4
− + − + − + − + − + − + − + − +
− − + + − − + + − − + + − − + +
− − − − + + + + − − − − + + + +
− − − − − − − − + + + + + + + +
No. of speeding violations
Average time in tired status (min)
Job completion time (min)
116.4 100.0 95.1 2.2 146.0 128.6 134.1 4.4 124.8 100.9 98.0 2.6 141.0 131.6 131.9 5.4
203.8 181.6 180.5 154.6 147.5 122.4 123.5 93.5 188.5 177.5 175.3 149.8 156.4 117.5 125.9 84.8
711.3 676.9 677.9 632.0 716.5 682.5 687.6 638.2 711.9 677.5 678.8 632.4 712.9 681.6 688.0 639.0
1 – number of trucks; 2 – number of excavators; 3 – rest duration; 4 – median safety attitude.
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Fig. 5. Main effects and factor interactions.
variable “No. of speeding violations” is calculated as e12v
1 = 2 −
(V − V ) + (V − V ) + (V − V ) + · · · + (V16 − V15 ) 7 4 3 8 12 11 4
(V2 − V1 ) + (V6 − V5 ) + (V10 − V9 ) + · · · + (V14 − V13 ) 4
(8)
Fig. 5 shows a summary of the results of the simulation experiments. As can be observed, even though many of the interactions are statistically significant, the actual magnitude of the interactions
diminish as the order of the interaction increases. Thus, the main effects dominate all the responses. 5.7. Implications Fig. 6 summarizes the results of the analysis. The manager is able to adjust the numbers of trucks and excavators, rest time and median safety attitude to balance activity completion time and safety performance. Safety performance is represented by the number of speeding violation and average time driven under fatigue, which are influenced by production pressure. Production pressure
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No of Speeding Violation
Expected production rate per driver -
Target completion time
+ Production + pressure + Completion time gap -
+
No of plants + No of trucks
Safety performance -
Median safety attitude +
Time driving under fatigue
Actual completion + time -
-
Available production time -
+ No of excavator
Rest time
Fig. 6. Influence diagram summarizing influence of factors on responses.
arise when the expected production rate per driver and/or when the gap between target completion time and actual completion time is increased. Actual completion time is decreased when the numbers of trucks and excavators are decreased. Although increasing rest time will reduce the average time driven under fatigue, it causes actual completion time to be increased. On the other hand, median safety attitude helps to reduce the effect of production pressure, but the effect is minor as compared to the effect of production pressure. Therefore, within the confines of the values stated in Table 4, increasing the numbers of trucks and excavators from 8 to 10 and 1 to 2 respectively, will help to reduce activity duration, and reduce production pressure on drivers. This indicates the importance of allocating sufficient plants for the activity. Increasing rest time and having intensive safety training targeted on improving safety attitude have effect on safety performance, but these effects can be easily negated by production pressure arising due to lack of resources.
In terms of analysis, the effects and interactions derived from the factorial experiments should be interpreted within the bounds of the factor levels. Future studies should utilize optimization techniques, such as genetic algorithms, to optimize the controllable factors automatically. Even though this study adopted AnyLogicTM to develop the case study, the conceptual framework and simulation approach can be implemented in any modeling tool or a general purpose programming language. Using a commercial software has many advantages in terms of having a more user-friendly development environment and technical support, but users can be restrained by the user interface and it also prevents collaborations among developers. The price of AnyLogicTM may also prevent researchers and user from adopting the proposed approach more willingly. Future research will explore how the hybrid simulation framework can be implemented in general purpose programming languages like Java or Python.
6. Limitations and future research
7. Conclusions
The study proposed a hybrid simulation framework for modeling construction activities so that safety behavior can be considered during planning. The case study showed how the hybrid simulation framework can be implemented. In view of the lack of application of factorial experimental approach in current safety management simulation studies, a detailed analysis was demonstrated. The case study was hypothetical and simple in many aspects, but this limitation does not affect the core purpose of this paper, which is to present the hybrid simulation framework for integrating safety behavior considerations into construction simulation models. Future research will implement the framework using actual data. One of the key challenges in implementing the framework is the modeling of the agents. This paper presented some suggested approaches, but future studies should review a wider range of fundamental psychological research on risk perception and safety behavior (e.g. Slovic, 2000, 2010) and select the appropriate model to represent safety behavior. In addition, more detailed calibration of assumed variables and comparison with experts and established models should be conducted to validate the simulation model. A “standard agent” can be created in the future to standardize the different behavioral, physiological and psychological aspects important to construction safety and productivity. With the “standard agent”, the complexity and effort for developing simulation models will be reduced significantly.
The construction industry needs to develop new approaches to improve its safety and health performance. One way to improve safety and health performance is to encourage early planning of construction processes and consider the possible impact on safety behavior. This study proposed a hybrid simulation framework to integrate safety behavior considerations in construction activity simulation. The hybrid simulation framework is based on a combination of discrete event simulation (DES), agent-based simulation (ABS) and system dynamics (SD). Since DES is the most common simulation approach in the construction industry, it is natural to use DES as the core of the proposed framework. However, in the proposed framework, the entities and resources in the DES are agents that are heterogeneous, able to make decisions and can interact with other agents. Due to the ability of SD to account for feedback and delays and its established approaches for modeling complex decision making, SD was used to model agents’ decision making and physiological processes. The DES–ABS–SD framework was demonstrated through a hypothetical earthmoving case study. A sensitivity analysis and a factorial experiment were conducted to encourage correct utilization of these analytical methods in construction safety management simulation. The case study showed how managers can utilize the hybrid simulation model to select suitable interventions to balance production and safety goals. In contrast to current construction planning simulation models, the proposed hybrid simulation approach allows integrates safety
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