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Integration of safety risk data.doc


Esmaeili, B., & Hallowell, M. (2013). Integration of safety risk data with highway construction schedules.Construction Management & Economics31(6), 528-541. doi:10.1080/01446193.2012.739288


The construction industry is characterized by a relatively high injury and illness rate compared to other industries. Within theconstruction industry, the highway construction and maintenance sector is one of the most dangerous. To improve safety in this sector, proactive methods of safety improvement and reliable risk data are needed. The safety risk quantification is the first step towards integrating safety data into design and planning. To enhance the current preconstruction safety practices,safety risks of highway construction and maintenance tasks were quantified and a decision support system was developed and tested that integrates safety risk data into the project schedules. Relative safety risks were quantified for 25 common highway construction tasks using the Delphi method. To ensure valid and reliable results, experts were selected according to rigorous requirements and multiple controls were employed to decrease cognitive biases. The data were incorporated into a decision support system called Scheduled-based Safety Risk Assessment and Management (SSRAM) that facilitates integration of safety risk data with project schedules. The resulting data-driven system produces predictive plots of safetyrisk over time based on the temporal and spatial interactions among concurrent activities. To test the utility of the decision support system and the validity of the underlying risk data, the system was tested on 11 active case study projects in the US. It was found that the database and associated decision support tool produce accurate and reliable risk forecasts that increase the viability of existing safety preconstruction activities. [ABSTRACT FROM AUTHOR

Intergration of safety risk data with highway construction schedules.pdf

Integration of safety risk data with highway construction schedules


Department of Civil, Environmental, and Architectural Engineering, University of Colorado at Boulder, 428 UCB,

1111 Engineering Drive, Boulder, CO 80303, USA

Received 29 March 2012; accepted 9 October 2012

The construction industry is characterized by a relatively high injury and illness rate compared to other

industries. Within the construction industry, the highway construction and maintenance sector is one of the

most dangerous. To improve safety in this sector, proactive methods of safety improvement and reliable risk

data are needed. The safety risk quantification is the first step towards integrating safety data into design

and planning. To enhance the current preconstruction safety practices, safety risks of highway construction

and maintenance tasks were quantified and a decision support system was developed and tested that inte-

grates safety risk data into the project schedules. Relative safety risks were quantified for 25 common

highway construction tasks using the Delphi method. To ensure valid and reliable results, experts were

selected according to rigorous requirements and multiple controls were employed to decrease cognitive

biases. The data were incorporated into a decision support system called Scheduled-based Safety Risk

Assessment and Management (SSRAM) that facilitates integration of safety risk data with project schedules.

The resulting data-driven system produces predictive plots of safety risk over time based on the temporal

and spatial interactions among concurrent activities. To test the utility of the decision support system and

the validity of the underlying risk data, the system was tested on 11 active case study projects in the US. It

was found that the database and associated decision support tool produce accurate and reliable risk forecasts

that increase the viability of existing safety preconstruction activities.

Keywords: Decision support systems, occupational health and safety, risk management, scheduling.


Typically, safety management activities take place

during the construction phase (e.g. job hazard analy-

ses and site audits). In recent years, new safety man-

agement strategies have been introduced that help the

project team to identify and control hazards during

design and preconstruction. However, according to

Szymberski (1997), the potential to influence site

safety and health conditions decreases exponentially

as the project commences. Recent research has con-

firmed these findings and indicates that the most

effective safety programme elements occur during the

programming and preconstruction phases (Rajendran

and Gambatese, 2009). Unfortunately, the current

methods for considering safety and health in these

early phases are inconsistent, informal, and based

primarily on intuition and judgment (Hallowell,

2008). Thus, there is clearly a need to enhance pre-

construction safety management strategies, to create

user-friendly tools, and to increase their use in all

sectors of the industry.

One of the preconstruction methods that has been

shown to be highly effective is the integration of the

safety aspect into project schedules using risk data

(Yi and Langford, 2006). Unfortunately, integration

is limited because of a lack of data for specific con-

struction work tasks and the lack of reliable tools that

interface with existing scheduling software. The cur-

rent study aims to test the theory that loading safety

risk data into the project schedule is practical and will

improve predictions of high risk work periods. The

objectives are to (1) quantify relative safety risk values

for common highway construction activities; (2)

*Author for correspondence. E-mail: behzad.esmaeili@colorado.edu

Construction Management and Economics, 2013 Vol. 31, No. 6, 528–541, http://dx.doi.org/10.1080/01446193.2012.739288

� 2013 Taylor & Francis

integrate these risk data into project schedules using a

novel decision support system; and (3) validate the

analytical procedure on case study projects.

This study focuses on risk quantification and risk

modelling for highway construction because the high-

way construction sector is one of the most dangerous

in the industry (Bureau of Labor Statistics, 2012). In

2005, this sector accounted for approximately 469

vehicle- and mobile heavy equipment-related deaths,

279 of which (59%) occurred in traffic work zones

(Center for Construction Research and Training,

2008). Furthermore, the Federal Highway Adminis-

tration (2004) estimates that a work zone fatality

occurs once every 10 hours and a work zone injury

occurs every 13 minutes. The presence of high-speed

traffic near work zones, prevalence of night-time

work, use of heavy equipment, exposure to weather,

and highly repetitive work tasks contribute to this rel-

atively high number of injuries (Bryden and Andrew,

1999; Arditi et al., 2005). Thus research is needed to

help practitioners to identify, analyse and respond to

high risk periods on highway construction projects.

Literature review

This study was guided by a large body of literature.

In particular, literature that focused on the safety-

schedule integration and construction engineering and

management (CEM) decision support systems (DSSs)

proved to be most helpful. This body of literature was

used to guide the risk quantification process and the

development of a framework for integrating safety risk

into project schedules. A review of the salient findings

from relevant literature is provided below.

Safety schedule integration

Integrating safety planning and management in early

phases of construction projects is essential to effective

injury prevention and the development of a culture of

safety (Tarrants, 1980; Sawacha et al., 1999). Coble

and Elliott (2000) argued that integration of safety

aspects into planning starts with considering safety

during the scheduling of a construction project. There

have been a multitude of studies that attempt to inte-

grate various forms of safety information with project

schedules. These studies can be divided into two gen-

eral categories: those that attempted to cover safety

planning, injury prevention and regulatory informa-

tion, and those that integrated risk data. The majority

of studies focused on the former because these safety

data, such as regulatory information, are readily

available and not difficult to obtain.

Safety-schedule integration began with the work of

Kartam (1997) who designed a framework for

integrating extensive safety knowledge (e.g. Occupa-

tional Safety and Health Administration (OSHA)

regulations) into critical path method (CPM) sched-

ules using Microsoft Project, Primavera P6, Primavera

Suretrack and Timeline. According to Hinze et al.

(2005) the major weakness of this initial effort was

that there was never any success in making a link

between the safety elements and the electronic sche-

dule. In response to this shortcoming, Hinze et al.

(2005) built upon this research effort by developing

SalusLink, a tool that allows project managers to

access textual safety data contained in databases

managed by Primavera P6 and Suretrack. Though a

working prototype was produced, the software is not

commercially available. Saurin et al. (2004) and

Cagno et al. (2001) took a different approach by

developing safety planning and control models that

attached injury prevention strategies and methods of

safety planning to scheduled activities.

In the past five years, researchers have attempted

to integrate risk data into project schedules as a

means to identify high risk work periods and

leverage scheduling controls to prevent periods of

excessive risk. For example, Wang et al. (2006)

developed a simulation-based model (SimSAFE) that

integrates expected injury cost data for each activity

in a network schedule. This stand-alone software

system allows safety managers to identify work zones

that are associated with relatively high risk as mea-

sured by cumulative potential accident costs. Yi and

Langford (2006) took risk integration a step further

by developing a robust framework for ‘safety

resource scheduling’ using patterns which are similar

to resource levelling. Although Yi and Langford

(2006) offered a strong framework for the integration

of safety risk data with project schedules, there were

no robust risk data as the database only included

fatalities that occurred as a result of falls from

height. Furthermore, Navon and Kolton (2006,

2007) created an automated monitoring and control

model that is capable of identifying fall hazards and

their location. The major limitations of this body of

literature are that there is not a robust safety risk

database and the interactions (i.e. compatibility and

incompatibility) among tasks were ignored.

Researchers have begun to model the interactions

among risk factors and create frameworks that inte-

grate detailed user-provided data into preconstruction

planning tools. For example, a series of studies mod-

elled the spatial and temporal interactions of concur-

rent work tasks by using information available in 4D

geographic models and user-provided data for ‘loss-of-

control events’ (Rozenfeld et al., 2009, 2010; Sacks

et al., 2009). The major limitation of these models is

that hazards related to each task must be identified

Safety risk data 529

and quantified by the user, which can be time inten-

sive, laborious and unrealistic in practice (Rozenfeld

et al., 2009). In another study, Hallowell et al. (2011)

adapted Yi and Langford’s (2006) model and sug-

gested a new framework to integrate safety risk data

into project schedules. In addition to integrating base-

level risks for individual tasks, this framework also

considered robust task interactions obtained through

the Delphi process. The limitation of this work was

that they did not test the applicability of the framework

on actual projects and base-level task risks were not

quantified. Thus, the current research aims to address

the limitations of the previous studies by quantifying

highway construction safety risks for common work

tasks and testing the efficacy of the framework

presented by Hallowell et al. (2011) on active projects.

CEM decision support systems

As computing technologies have improved, increased

attention has been paid to the development of com-

puter applications that increase the speed and quality

of decision making. One category of these tools is

decision support systems (DSSs), which are defined

as, ‘an interactive IT-based system that helps decision

makers utilize data and models in making their deci-

sions’ (Carter et al., 1992, p. 3). Typically, the two

main objectives for using DSSs are: performing a

given task in the decision-making process more

quickly and with fewer resources (efficiency); and

improving the quality of the outcome of the decision-

making process (effectiveness). In addition, DSSs

help a manager to make more informed decisions,

consider a multitude of criteria and alternatives,

reduce the time needed to make an effective decision,

and focus attention on the most important elements

of a scenario. They also reduce complexity of the

problem to a manageable level and reduce uncertainty

(Carter et al., 1992). In CEM, DSSs have been uti-

lized in many areas such as: resource sharing (Perera,

1983); prequalifying subcontractors (Russell et al.,

1990); optimizing heavy lift planning (Lin and Hass,

1996); resource levelling (Leu et al., 2000); making

go/no-go decisions for international projects (Han and

Diekmann, 2001); selecting appropriate project deliv-

ery methods (Molenaar and Songer, 2001); schedul-

ing steel fabrication (Karumanasseri and AbouRizk,

2002); and providing guidance during dispute resolu-

tion (Palaneeswaran and Kumaraswamy, 2008).

In addition to the applications mentioned above,

some DSSs have been developed to enhance decision

making in the area of safety. For example, Kak et al.

(1995) developed a knowledge-based program to

facilitate access to the explicit safety knowledge on

construction sites. Their program searches applicable

safety regulations (e.g. OSHA) for a particular task

and provides suggestions to improve compliance.

Gambatese et al. (1997) presented a tool for incorpo-

rating safety-related issues in the design phase of a

project called ‘Design for Construction Safety Tool-

Box’, which has the ability to identify project-specific

hazards and provide design suggestions to mitigate

those hazards. Recently, Hadikusumo and Rowlinson

(2004) applied a visual reality concept to develop a

design for safety tool to capture tacit knowledge of

safety professionals. The results contribute to the

current arsenal of CEM and safety tools by providing

an applied DSS that integrates safety risk data into

project schedules.

Point of departure

To build upon safety risk management research, the

relative safety risk of common highway reconstruction

tasks was assessed and the efficacy of a DSS that inte-

grates safety risk data into project schedules was

tested. A thorough review of relevant literature

revealed no study that has directly quantified highway

reconstruction safety risks or attempted to assess tem-

poral models for safety risk integration using actual

data. It is expected that the findings presented will

aid project managers in their preconstruction safety

management activities and will be especially effective

for safety managers who are responsible for multiple

concurrent projects.

Research methods

The research objectives were achieved in three distinct

phases. In the first phase, the Delphi method was

employed to quantify relative safety risks. In the

second phase, a graphical user interface was developed

in MATLAB, called Scheduled-based Safety Risk

Assessment and Management (SSRAM), that is capa-

ble of creating temporal safety risk profiles for highway

construction projects. Finally, the output of the system

was validated by employing a Multi-Attribute Utility

Assessment (MAUA) technique and conducting 11

case studies. The following sections discuss the details

of the research methods employed in these three


Phase I method: risk quantification

In order to develop an appropriate scope for data col-

lection, clear definitions of common highway con-

struction work tasks were needed. Therefore, the 25

highway tasks identified and described by Pandey

530 Esmaeili and Hallowell

(2009) and refined by Hallowell et al. (2011) were

used as a foundation. To quantify the relative risk val-

ues for these tasks, the Delphi method was selected.

The traditional paradigm in risk quantification

adopted by Brauer (1994) and Hallowell and

Gambatese (2009) was followed where frequency and

severity ratings for each task are solicited from an

expert panel through multiple rounds of surveys and

controlled feedback.

The Delphi method was chosen for obtaining

safety risk values for six main reasons. First, there

were no objective highway repair and maintenance

safety risk data available from government databases.

The common national databases such as Occupa-

tional Safety and Health Administration Integrated

Management Information System (OSHA IMIS,

n.d.) and National Institute of Occupational Safety

and Health Fatality Assessment and Control Reports

(NIOSH FACE, n.d.) include only high severity

injuries and do not provide enough information

regarding the task performed when the injury

occurred. Second, according to Gyi et al. (1999), the

validity of statistical data obtained from accident

reports is significantly compromised by underreport-

ing, especially for minor injuries. Third, Snashall

(1990) stated that accident report processes are not

consistent between and within companies

(e.g. definition of construction activities) such that

empirical data cannot be easily interpreted and com-

pared. Fourth, accidents happen in a complex sys-

tem created by interrelated worksite characteristics

that cannot be separated from the project context

(Mitropoulos et al., 2005). Fifth, according to Dijk-

sterhuis et al. (2006), intuitive decision processes like

Delphi that use heuristic principles lead to accurate

risk estimates in complex scenarios. Finally, Delphi

is a rigorous process that allows researchers to obtain

unbiased data using the judgment of qualified

experts, which has been used successfully for risk

quantification in similar studies (e.g. Hallowell and

Gambatese, 2009; Hallowell et al., 2011).

The Delphi method was developed by Rand

Corporation for the US Air Force in late 1940s to

elicit reliable and unbiased judgments from a group of

experts by conducting an iterative process and

providing controlled feedback (Helmer, 1967; Lin-

stone and Turoff, 1975). The Delphi method involves

assembling qualified experts, developing appropriate

questionnaires, and conducting multiple rounds of

surveys with controlled feedback between rounds to

achieve consensus (Cabaniss, 2001; Hallowell and

Gambatese, 2010). This method is applied under the

assumption that the collective expertise of the panel is

superior to the judgment of individuals (Hogarth,

1978; Boje and Murnighan, 1982; Hill, 1982).

The Delphi process was conducted in two rounds

where expert panellists were asked to provide inde-

pendent frequency and severity ratings for each of the

25 highway construction tasks. In order to maintain

consistency, the authors have adopted an objective

risk scale created by Hallowell and Gambatese (2009)

that incorporates a complete spectrum of frequency

and severity scales (see Table 1). The severity scale

ranges from negligible injury to fatality and the fre-

quency scale ranges from one incident occurrence

every six minutes (0.1 w-h) to one incident occur-

rence every 100 million or more worker-hours (>100

million w-h). After the first round of surveys, the data

were aggregated and the level of consensus was mea-

sured and evaluated. In the second round, panellists

were asked to review the median responses from the

first round and provide final ratings. As will be dis-

cussed, a third round of data collection was not

needed because the target consensus was achieved in

the second round.

Selection of expert panellists

As the number of panellists in a Delphi study

increases, the accuracy of the results also tends to

increase (Murphy et al., 1998). In a review of past

Delphi studies, Rowe and Wright (1999) found that

the number of panellists has ranged from 3 to 80. As

noted by Linstone and Turoff (1975) factors such as

the expected volume of the data, time constraints,

and the number of experts available can affect the

appropriate number of panellists. A relatively large

panel was desired to quantify risks for the 25 highway

construction tasks that can be performed in a variety

of work environments.

Careful attention was paid to ensure that all

panellists were highly qualified. The expert panel was

Table 1 Frequency and severity scales (adapted from Hallowell and Gambatese, 2009)

Frequency Severity

Worker hours per incident Subjective level Score

>100 million Temporary discomfort 2

10–100 million Persistent discomfort 4

1–10 million Temporary pain 8

100 000–1 million Persistent pain 16

10 000–100 000 Minor first aid 32

1000–10 000 Major first aid 64

100–1000 Medical case 128

10–100 Lost work time 256

1–10 Permanent



0.1–1 Fatality 26 214

Safety risk data 531

assembled using the 165 contacts provided

by the National Work Zone Safety Information Clear-

inghouse (http://www.workzonesafety.org/expert_con-

tacts/browse/all_experts). Because this website provides

no information regarding the qualification of any of the

contacts as ‘experts’, the research team independently

validated expert status with an introductory survey

using guidance provided by Hallowell and Gambatese

(2010). Of the 165 individuals contacted, 75 (45%)

responded, and 27 (36%) were qualified as experts.

According to Moser and Kalton (1971), this response

rate is acceptable for Delphi studies.

The resulting pool of individuals averaged over 25

years of highway construction safety experience. Over

80% of respondents had a Professional Engineering

(PE) licence, were a Certified Safety Professional

(CSP), or had at least a bachelor’s degree in a related

field and all respondents were upper-level managers

or executives (e.g. corporate safety manager, director

of research, and senior project manager). It should be

noted that, despite the relative large publication lists

of some participants, the panel was largely profes-

sional in nature. This was preferred as accurately

quantifying relative risks relies upon a wealth of

professional experience.

Number of iterations and feedback

One of the objectives of the Delphi process is to reach

consensus, which can be achieved by conducting mul-

tiple iterations of questionnaires and providing anony-

mous feedback between rounds. Two to seven rounds

have been used in the previous large-scale Delphi stud-

ies (Dalkey et al., 1970). According to Jolson and

Rossow (1971), iterations can be terminated when the

changes in variance are no longer significant. The

research team administered two rounds of surveys

because the size of the expert panel (27) exceeded the

minimum size recommended (eight) for traditional

Delphi studies (Brockhoff, 1975; Boje and Murnighan,

1982) and there was a high degree of consensus

among the experts after the second round.

Cognitive biases

In order to decrease the complexity of probability

assessment, many individuals use a limited number of

heuristic controls (Tversky and Kahneman, 1974).

However, relying on these heuristics may produce sys-

tematic errors in judgment known as cognitive biases.

Despite their importance, cognitive biases have not

received adequate attention in previous Delphi studies

(Hallowell and Gambatese, 2010). The following

eight biases were identified and controlled: collec-

tive unconscious, contrast effect, neglect of probabil-

ity, Von Restorff effect, myside bias, recency effect,

primacy effect and dominance (Hallowell and

Gambatese, 2010).

To minimize the potential influence of cognitive

biases, several controls were implemented. First,

respondents were kept anonymous. Maintaining the

anonymity of respondents reduces the impact of

group dynamics, dominant personalities and the

bandwagon effect (Manoliadis et al., 2006). Second,

randomizing the question order of the surveys mini-

mizes the potential influence of primacy and contrast

effects (Hallowell and Gambatese, 2010). Third, the

median ratings from the previous rounds were

provided as feedback, which significantly reduces vari-

ability among panellists (Martino, 1970). Fourth,

experts were asked to rate frequency and severity lev-

els separately to avoid the neglect of probability bias.

Finally, to ensure internal validity and to enhance the

reliability of the results, all experts were provided with

consistent task names and descriptions.

Phase II method: decision support system


One of the structured design methods to develop a

decision support system (DSS) is prototyping (Andr-

iole, 1989). The prototyping principles established

by Boar (1984) were followed where the develop-

ment of a DSS involves input from perspective users

and is refined with professional feedback in an itera-

tive process. Following the guidance provided by

Andriole (1989), the first step in designing the DSS

involved identifying the tasks that the system must

perform and the requirements of the user. According

to Boar (1984), 20 to 40% of DSS’s problems can

be attributed to the design process. Well-defined

requirements will make a link between users, tasks

and organizational needs (Andriole, 1989). Here, a

quick prototype was made and its features were

modified by receiving feedback from the users in an

iterative process. In the second step of the DSS

development, the safety risk data were mathemati-

cally integrated with activity sequences. The data

from Hallowell et al. (2011) and those established

through the Delphi process were used to populate

the theoretical model shown in Equation 1. In the

subsequent phase, the research team tested this

model with active construction projects in the US.

½SFTask�1�n ¼ ½RIndividual�1�25 � ð½RInteraction�25�25 � ½XSchedule�25�nÞ ð1Þ


[RIndividual] is a matrix that includes safety risk values

for individual tasks; [RInteraction] is a matrix that includes

the safety risk interactions among tasks from Hallowell

532 Esmaeili and Hallowell

et al. (2011); [XSchedule] is a matrix that includes 0’s and

1’s depending on whether or not particular activities

are scheduled for a given time period. If in time t,

activity i is being performed, then Xit=1, otherwise Xit = 0. [SFTask] is the resulting safety risk matrix that

includes the resulting risk for each time period.

Phase III method: risk data and DSS validation

One of the methods that has been used extensively to

decompose the general measure of effectiveness of a

DSS is Multi-Attribute Utility Assessment (MAUA)

(Adelman and Donnell, 1986; Sage, 1991). MUAU is

a formal structure that maps different measures of

effectiveness against one another and is defined as,

‘scoring and weighting procedures to evaluate the

overall utility of a knowledge-based system to users

and sponsors’ (Adelman and Riedel, 1997, p. 37).

This method has been used to evaluate similar DSSs

in several studies in the past (e.g. Adelman and

Ulvila, 1991). The total measure of effectiveness is

the weighted sum of all the utility scores, shown as

Equation 2 (Adelman, 1992):

UðiÞ ¼ W1: uðxi1Þ þ W2: uðxi2Þ þ � � � þ WJ: uðxijÞ ð2Þ

where: U(i) is the overall utility for alternative i; wj is

the cumulative relative weight on attribute j; u(xij) is

the utility scale value for alternative i on attribute j.

Figure 1 presents the hierarchy of effectiveness

criteria that was created from existing literature and

discussions with potential users. The three main

evaluation criteria were: usability, applicability and

reliability. Usability was defined as the system’s ease

of use, response time, ease of training and graphic

displays; applicability was defined as the extent that

the program and its output can be used by a

construction firm to enhance decision making and

resource allocation; and reliability was defined as the

predictive accuracy of the system. It is notable that

predictive accuracy of the framework and developed

DSS relies heavily on the reliability of the safety risk

database. In other words, the reliability scores

obtained from the MAUA process are an indicator of

the validity of quantified safety risks.

In order to determine the total utility of the system,

the research team used a case study approach where

relative weights of the criteria and scores for the sys-

tem were obtained through interviews with prospec-

tive users. Case studies were chosen because the

sample size and randomization requirements of a true

experiment were not feasible (Adelman, 1992) and

case studies are appropriate for studying new strate-

gies in context (Yin, 2003). The main units of analy-

sis were active or recently completed projects.

To obtain a representative sample of US highway

construction projects, highway construction firms that

were members of the Associated General Contractors

(AGC) or the Colorado Asphalt Pavement Associa-

tion were asked to participate. Of the 39 contractors

that were contacted, a total of five firms agreed to

provide project data and participate in a series of

interviews. The revenue of the companies ranged

from $50 million to $2.5 billion with the average of

$583 million. The companies, on average, had more

than 700 workers and had been in the highway con-

struction business for over 50 years.

Overall utility


Extent of use


Usefulness of output

Impact on the current procedures



General ease of use

Ease of training

Ease of data entry


Graphical features

Figure 1 Hierarchy of measures of effectiveness

Safety risk data 533

According to Yin (2003), the number of cases com-

pleted and the quality of pattern matching have signifi-

cant impact on the validity and reliability of the

results. Literature suggests that four to 10 cases will

provide valid and reliable data as long as pattern

matching is strong and data are collected consistently

among cases (Eisenhardt, 1989; Yin, 2003). To ensure

adequate data, a total of 11 case studies were con-

ducted. The demographics of these cases are summa-

rized in Table 2. As shown in Table 2, a diverse set of

projects is included ranging from large scope and long

duration to small scope and short duration. Also, a

higher number of projects were located in Colorado,

which limits the external validity of the results.

In order to increase the reliability and internal

validity of the study, a specific case study protocol

was implemented. The following four steps were

conducted for every case study:

(1) Interviews were conducted with the construc-

tion project manager or safety managers to

quantify the relative weights of the attributes by

conducting pairwise comparisons between crite-

ria. The interviewees were asked to use a pro-

vided comparison scale that was based on

previously successful studies described by Saaty

(1980). A consistency ratio was then used to

ensure that each respondent’s ratings were inter-

nally consistent. As suggested by Shapira and

Goldenberg (2005), participants were asked to

repeat the rating process if their internal consis-

tency ratio exceeded 0.1. In other words, if an

individual’s pairwise comparisons among crite-

ria resulted in 10% or greater internal inconsis-

tency, they were asked to repeat the process

until their ratings were in agreement. An

acceptable internal consistency ratio indicates

that there is no intolerable conflict in the com-

parisons of a participant’s response (Shapira and

Goldenberg, 2005).

(2) After the weights had been found, interviews

were conducted to determine the DSS’s scores

for different criteria. In order to gather opin-

ions about the usability and applicability of the

DSS, the operation of the system was demon-

strated to the participants. Immediately follow-

ing the demonstration, the users were asked to

complete an 18-question survey (two questions

for each attribute) that addressed all criteria

shown in Figure 1. The participants were asked

to rate the system’s performance on a scale

from 0% (very poor performance) to 100%

(very strongly performance), with 50% being


(3) The project schedule was then obtained and

the project manager was interviewed to ensure

that the research team had an accurate under-

standing of the actual activities that were per-

formed on the project. With the project

manager, the tasks and durations were matched

with the tasks described in Hallowell et al.

(2011). This mapping process was required

because the DSS was built around the data

from previous research and consistency of task

names was required for the system to operate

effectively. Once the tasks were mapped, the

schedule integration function of the DSS was

used to produce a safety risk profile. The con-

struction project manager or safety manager

was then asked to compare the risk profile

Table 2 Projects demographic information




(million $)



Percent of

completion (%)



Method of



injuries Location

1 150 50 88 Bid/Build Unit price 2 California

2 110 30 65 Design/


Lump sum 0 Utah

3 48 36 94 Bid/Build Monthly


2 California

4 5.5 4 100 – Pay


0 Colorado

5 4.5 6 100 Bid/Build Unit price 0 Washington

6 0.32 1.5 99 Bid/Build Unit price 0 Colorado

7 0.38 3 90 Bid/Build – 0 Colorado

8 0.66 5 100 – – 0 Colorado

9 0.37 10 100 Bid/Build Unit price 0 Colorado

10 0.49 1.5 100 Bid/Build – 0 Colorado

11 1.5 3 100 – Pay


0 Colorado

534 Esmaeili and Hallowell

created by the DSS with the actual level of risk

and provide an approximate percentage agree-

ment with the system. The interviewees aimed

to compare the pattern of the risk profile with

near misses and the actual hazards that existed

during the work. In fact, the current study did

not aim to predict injuries in the jobsite.

Rather, the focus was on predicting high risk

work periods where the potential for injury is

relatively high. It is important to distinguish

the difference between hazards and accidents.

For example, if a worker was exposed to adja-

cent traffic, there were significant hazards even

though no injury was realized.

(4) The final step of the case study involved a fol-

low-up questionnaire that included open-ended

questions that gave the participants an oppor-

tunity to share their thoughts on the perceived

strengths and weaknesses of the system.


Phase I results: risk quantification

All 27 expert panellists provided complete responses

to the Delphi surveys in the first round and the abso-

lute variance of responses for frequency and severity

were 0.733 and 0.838, respectively. Once the data

were aggregated and summarized for the panel, sec-

ond rounds of surveys were administered. In the sec-

ond round, the median responses from the previous

round were provided to the panellists and they were

given the option to agree with the other group’s col-

lective assessment or provide a new rating. Of the 27

surveys that were sent in the second round, 24 sur-

veys were returned resulting in 89% response rate.

Absolute variances of responses in the second round

were 0.198 and 0.191 for frequency and severity,

respectively. Because the established consensus was

achieved in the second round, there was no need for

a third round. Additionally, the median ratings did

not change between rounds, which is evidence of

strong internal validity.

To facilitate calculations, the frequency ratings

were converted from a range of values with units of

worker-hours per incident to a single point value with

units of incidents per worker-hour. The mean value

was selected as a point value and inverted to obtain a

number with appropriate units. For example, if the

Delphi panel rated the average frequency as 10–100

w-h/incident, the mean value, 55 w-h/ incident, was

inverted (0.018 incidents/w-h) to determine the fre-

quency value for that particular risk and activity.

Severity values were not changed from the severity

scale in Table 1.

The frequency ratings ranged from 1.8E-8 to 0.018

incidents per worker-hour and the severity ratings

ranged from 4 to 256 units on the severity scale. Unit

risk scores were calculated by multiplying the average

frequency scores by the average severity scores. The

resulting data for the 25 work tasks are provided in

descending order of relative risk in Table 3. In this

table, risk is described in terms of units of severity per

worker-hour (S/w-h). The task ‘construction zone

traffic control’ has the highest unit risk (0.047 S/w-h)

while ‘watering and dust palliatives’ (1.8 � 10-8 S/w-h) and ‘install field facilities’ (1.8 � 10-8 S/w-h) have the lowest unit risk.

Phase II results: DSS development

In order to provide a user friendly environment to

integrate safety risk data and project schedules, a

graphical user interface (GUI) was developed in

MATLAB called Scheduled-based Safety Risk Assess-

ment and Management (SSRAM). MATLAB was

chosen for two main reasons: it is a strong program-

ming language to develop graphical user interfaces

and it allows the research team to make an active con-

nection between standard project management soft-

ware and safety risk databases. An applied DSS for

integrating safety risk data into project schedules

should include two main capabilities: (1) receiving the

schedule from the user; and (2) creating the safety

risk profile. These capabilities were considered during

the development. Although the DSS can be used to

manually add tasks, start dates and end dates to build

the schedule, the researchers built a bridge between

Primavera 6, MS Project and the DSS using MS

Excel as medium to increase efficiency. After entering

projects to the program, the user can save the sched-

ules as M-file ( ⁄ .m or

⁄ .matt). The resulting DSS

(SSRAM) is a knowledge-based system with a

schedule integration engine.

The conceptual formulation and computational

process of the SSRAM is shown in Figure 2. The

safety risk database includes the base-level safety risk

(obtained from Delphi panel in the first phase) and

the safety risk interactions (a 25 � 25 matrix from Hallowell et al., 2011). The user can insert the

schedule manually or import it from scheduling

software (e.g. Primavera 6). Once the schedule is

entered, the SSRAM loads safety risk data from the

database to the imported schedule using Equation 1

and subsequently plots the risk profile. There are sev-

eral practical applications of the safety risk profiles.

For example, the risk profiles can be used to identify

high risk periods during the project, the safety risk

can be levelled utilizing float of activities, or the pro-

ject manager can allocate safety resources according

Safety risk data 535

to the risk profile. In addition, the program is able to

create safety risk profiles for multiple projects or port-

folios of a company. This is important because safety

managers for highway construction companies must

often manage multiple concurrent projects. Using the

SSRAM helps them to strategically allocate their time

and safety resources.

Phase III results: risk data and DSS validation

The results of pairwise comparisons made by the

user group are shown in Table 4. The users

believed that the reliability, the general ease of use

of the program, and the usefulness of the output

are the most important attributes. Because the

usability and applicability have a subset of

Table 3 Safety risk data for common highway reconstruction work tasks

Task name Frequency score (incident/w-h ⁄ E-5) Severity score Unit risk scores (S/w-h

⁄ E-5)

Construction zone traffic control 1800 256 4700

Install traffic control devices 18 64 4700

Installing flexible pavement/patching 18 64 12

Pavement marking 18 64 12

Seal joints and cracks 18 64 12

Excavation 18 64 12

Install culverts, drains, sewers 18 64 12

Install culvert pipe and water lines 18 64 12

Reset structures 18 64 12

Heat and scarifying 18 64 12

Survey 1.8 32 5.8

Clear and grub 0.18 16 2.9

Recycle cold bituminous pavement 0.18 16 2.9

Install curb and gutters 0.18 16 2.9

Install rigid pavement (concrete) 0.18 16 2.9

Install cribbing 0.18 16 2.9

Recondition bases (compaction) 0.18 16 2.9

Install water control devices 0.18 16 2.9

Lay aggregate base course 0.18 16 2.9

Mobilization/demobilization 0.18 16 2.9

Prime, coat, rejuvenate pavement 0.18 16 2.9

Demolition of existing pavement 0.18 16 2.9

Landscape 0.18 16 0.029

Install field facilities 0.0018 4 0.0073

Watering and dust palliatives 0.0018 4 0.0073

Integration procedure


[SF Task

] 1×n =[RIndividual]1×25 ×([RInteraction]25×25 ×[XSchedule]25×n)

1. Safety risk profiles

Project’s schedule

1. Inserting manually

2. Importing from scheduling software

2. Risk threshold

Safety risk database

1. Base level safety risks (Table 2)

2. Safety risk interactions (Hallowell et al., 2011)

Figure 2 SSRAM’S framework

536 Esmaeili and Hallowell

attributes, the relative weights for the higher tier

attributes were computed by finding the products of

the subsets (see Table 4). For example, the relative

weight of workload (0.17) was multiplied by the rel-

ative weight assigned to usability (0.15) to reach

the total ‘workload’ weight of (0.03). Once the

weights of the criteria were found, they were multi-

plied against the scores and the resulting products

were summed to compute the global utility factor

(0.67). This number can be interpreted as the value

that the SSRAM adds to the current safety manage-

ment practice, which ranges from a score of 1 that

corresponds to a revolutionary product that

completely changes current industry practice and is

perfectly executed to a score of 0 where no value is

added. One should note that the interviewees, who

rated the output of the program, were not informed

of the analytical procedure that resulted in the out-

put. Therefore, any inconsistencies among the inter-

viewees’ opinions and the Delphi experts’ judgment

decrease the actual utility of the system as a whole.

In fact, the proposed validation methodology tested

the ability of the SSRAM to forecast hazardous

conditions. Considering the complex and dynamic

nature of the construction projects, reaching 100%

accuracy was not realistic.

Although the research team was satisfied with the

results, there are no similar DSS validation studies

to compare against. Fortunately, the follow-up inter-

view questions validated the SSRAM global utility

score because respondents indicated that the pro-

gram is easy to use and greatly improves precon-

struction safety management at the project and

program levels.


Though the results have the potential to impact posi-

tively on preconstruction safety management, there

are several limitations of the research.

(1) The risk quantification portion of the study

required that the Delphi panel assume typical

conditions in their ratings. Consequently, the

data are limited by the fact that actual conditions

such as weather, crew safety culture and fatigue

affect the true risk values (Manu et al., 2010).

Because there are large numbers of external risk

factors that can affect the base-level risk of activ-

ities, considering their effects was unrealistic.

The influence of external factors may explain

some of the variation between the predicted

values and actual values during the case studies.

(2) The risk values were estimated as a single point

estimate for each task. The average risk values

may not capture all characteristics of risk or, as

Kaplan and Garrick (1981) stated, a single

number cannot communicate risk effectively

due to the great loss of information.

(3) Although several measures were employed to

decrease cognitive biases in the Delphi process,

there are still several limitations to the frequency

and severity values provided by experts. One of

the common limitations is related to accidents

with low probability of occurrence and high

impacts. Taleb (2007), one of the prominent

researchers in this area, called these extreme

events, ‘Black Swans’. He stated that it is almost

impossible to predict extreme events because

they do not have predecessor events (Taleb,

Table 4 Weights of measures of effectiveness and their consistency ratio

Measures of effectiveness Relative weights Total weights Scores Utility

Usability 0.15 – – –

Applicability 0.25 – – –

Reliability 0.60 0.60 0.66 0.40

Usability General ease of use 0.31 0.05 0.69 0.03

Ease of training 0.26 0.04 0.75 0.03

Ease of data entry 0.17 0.03 0.66 0.02

Workload 0.17 0.03 0.60 0.02

Graphical features 0.07 0.01 0.68 0.01

Applicability Extent of use 0.23 0.06 0.58 0.03

Usefulness of output 0.36 0.09 0.73 0.06

Impact on the current procedures 0.29 0.07 0.61 0.04

Performance 0.13 0.03 0.70 0.02

Total 0.67

Safety risk data 537

2007). In fact, predicting these low-probability,

high-impact events is extremely difficult and

more attention should be paid to reduce the vul-

nerability of the system towards their conse-

quences than anticipating them (Taleb, 2004).

(4) The safety risks were quantified for only 25

tasks. In order to add a new task to the schedule,

its base-level safety risk and the interactions with

other tasks must be quantified separately.

(5) The external validity is limited because the

data and DSS were validated on projects in

Colorado, Oregon, California, Utah and Wash-

ington, with a higher number in Colorado.

Although it is expected that projects are repre-

sentative of the US, the scope of inference is

theoretically limited only to these states.

Despite these limitations, the resulting data and

SSRAM significantly furthered knowledge and were

accurate and useful enough to gain favourable

responses from industry users.

Conclusions and recommendations

According to Esmaeili and Hallowell (2012a), the

construction industry is saturated with respect to

traditional injury prevention strategies and new safety

innovations are needed. Previous research has estab-

lished that the potential to prevent construction inju-

ries is at its highest during the preconstruction phase

and decreases exponentially as a project progresses

(Gambatese et al., 1997; Szymberski, 1997). Tradi-

tionally, preconstruction safety improvement tech-

niques such as designing for safety have faced

significant barriers that stem from the fact that they

are largely designer-controlled (Hinze and Wiegand,

1992). One of the contractor-controlled practices that

can be used to overcome this shortcoming is safety-

schedule integration.

Although there have been attempts to integrate

safety risk data into project schedules (e.g. Yi and

Langford, 2006; Sacks et al., 2009), these attempts

were not successful because of the absence of a valid

and reliable safety risk database. This limitation was

addressed by quantifying the relative risk of 25 com-

mon highway construction tasks and combining this

information with risk interaction data obtained by

Hallowell et al. (2011) to populate a new risk integra-

tion framework. To facilitate implementation, validate

the risk data, and test the utility of the underlying

framework, the research team created a safety DSS

(SSRAM) using MATLAB. The resulting tool inter-

faced with Primavera P6 to produce plots of safety risk

over time for single or multiple concurrent projects.

The tool was then tested on 11 case study projects.

This validation effort revealed that the data are valid

and reliable despite recognized limitations and the

SSRAM has the potential to improve safety resource

optimization and preconstruction safety management.

To summarize, the authors tested the validity of the

risk database as an input and reliability of the risk pro-

files generated by integrating base-level risk data and

risk interaction data with highway project schedules.

The traditional safety management approach

involves investing safety resources such as time and

money at a uniform rate throughout the lifespan of a

project (Rozenfeld et al., 2009). However, because

physical conditions change rapidly, safety risk levels

may also fluctuate. Consequently, uniform resource

allocation to safety within a project and among

projects may not be the optimum strategy. One of the

viable solutions for this problem is to apply lean

thinking to the construction process (Womack and

Jones, 2003) so that injury prevention practices can

be treated as production control activities (Rozenfeld

et al., 2010). According to Rozenfeld et al. (2010),

the ability to predict fluctuating safety risk levels is

essential to practical lean-based safety management.

The findings can be used to predict safety risk levels

and use schedule float to distribute or concentrate

risk. In addition, risk profiles enable thoughtful safety

planning and effective allocation of safety resources in

a single project or multiple projects.

In addition, the data and framework presented can

be used by project managers to enhance preconstruc-

tion safety management by identifying high risk peri-

ods. In response, safety managers can plan for extra

precautionary measures during these high risk periods

(e.g. lane closure), develop customized injury preven-

tion strategies, or at a minimum, inform workers of

the tasks and interactions known to cause high risk

periods. In addition to using the schedule-based tech-

nique described, the writers also recommend that

practitioners focus attention on high risk work tasks

(e.g. construction zone traffic control, installing traffic

control devices and excavation).

To address the aforementioned study limitations

the writers suggest three complementary research

efforts. First, new safety risk quantification methods

should be explored to produce robust and reliable risk

data independently from specific tasks, trades and

construction objects. For example, Esmaeili and

Hallowell (2011, 2012b) utilized genome concept to

quantify safety risks at attribute level independently

from tasks and objects. Population of this method can

be a major step towards a universal safety risk

assessment in the construction industry. Second,

researchers should consider modelling probability dis-

tributions of accident occurrence for individual tasks.

538 Esmaeili and Hallowell

Using probability distributions instead of the average

estimated points allows an individual to consider

uncertainty in the data and investigate its propagation

thorough the model (Fischhoff et al., 1984). Finally,

the relative impacts of environmental risk factors on

the base safety risk values must be better understood

to create robust models. The external risk factors may

include the intensification of risk due to night-time

work, exposure to weather, and adjacent traffic.


The writers would like to thank Bentley Systems for

the resources and high quality feedback during the

project and all of the Delphi panellists for their

enthusiastic participation.


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