Construction Research Congress 2018: Safety and Disaster Management

The construction industry is a vast industry in India which contributes to 11% of GDP which has a good impact on national economy growth and it is an essential part of the infrastructure and industrial development. But construction professionals face an unreasonable stress that generates to physiological, sociological, and psychological strain effects; which greatly influences their behaviours and safety performance. This paper identifies the causes of the stress among construction professionals and enlists the factors that affect the productivity of their job, the work-life balance of construction professionals, their stability and job control in the construction industry. This paper emphases the stress among the construction professionals in Chennai, thorough the study of different literature studies, qualitative and quantitative analysis on data regarding. For this questionnaire survey was administered to practitioners, and 130 responses were received. Statistical analysis confirmatory factor analysis, correlation analysis were conducted and the results, conclude that women professionals have more stress than men. Based on designation site engineers possess high stress. The results brought out the fact that excellent communication and team support may help to keep the relationship between employees, which contribute to reducing the stress level among the professionals.

The significant amount of safety data being collected on construction sites provides a valuable source of information for researchers seeking to better understand the root causes of accidents using advanced-analysis methods such as data mining and machine learning techniques. However, evaluating the predictive performance of these methods can be challenging. While simple measures such as total accuracy have been widely used to assess the classification performance of these algorithms, studies show that relying solely on these individual measures can be misleading. One of the techniques that can be used to organize classifiers and visualize their performance is receiver operating characteristics (ROC) curves. ROC curves are commonly used in machine learning and data mining studies to improve medical decision making; however, their application in construction safety is minimal. This study evaluates the application of ROC curves to more accurately interpret the output of machine learning methods, because understanding the mechanism of ROC curves will improve the classification performance of machine learning methods (i.e., AdaBoost) when predicting the degree of construction injuries. To achieve this goal, more than 700 accident reports were collected from national databases and analyzed via multiple machine learning techniques. The findings of this study indicate that applying ROC curves can yield the selection of better classification criteria and can reduce the cost of classification errors. The method is particularly beneficial when the costs of errors are unequal or when the data is highly imbalanced.

While construction is a major contributor to the U.S. economy, it is reported that construction trades workers retire early due to work-related musculoskeletal disorders (WMSDs) and overexertion involving lifting, bending and twisting more often than other occupational groups. To investigate and prevent WMSDs especially the low back pain in construction work, practitioners and researchers adopted several engineering and management measures. The examples include reducing the material mass, increasing the initial lifting height, working in a team, and arranging rest time during the work. However, the outcome has not been optimistic and promising. In this study, a wearable exoskeleton robot was designed and developed to habituate construction workers to the safe postures recommended by the National Institute for Occupational Safety and Health (NIOSH). Subjects were tested to measure and analyze their motions and postures while they were performing basic lifting tasks utilizing a full body integration system of inertia measurement units. From the test, it was identified that the actuators of robot effectively constrained worker’s body postures within a safe range when the system detects awkward postures from a worker. This demonstrates that the proposed wearable robot can effectively keep workers in safer postures while they are performing construction tasks. Also, the proposed postural training through physical interventions is expected to effectively sustain corrected postural habits for longer periods.

Safety training has a direct impact on the prevention of construction accidents, and increasingly dynamic and complex working conditions require that construction workers and professionals acquire the safety-related knowledge they need to make safety conscious decisions. Currently, virtual modeling methods—such as building information modeling (BIM)—provide the means for visualizing the associated safety conditions present during construction projects and for simulating real working environments for safety-training purposes. However, these models use unrealistic computer-generated simulations of a construction environment and therefore do not provide a full depiction of real-world environments in terms of time, physical space, and material properties. One of the emerging technologies that can address this limitation is panoramic augmented reality. In this paper, 360° panoramas are used for visualizing complex situations on real construction sites, and these panoramas can be augmented with different layers of safety-related information. Panoramic augmented reality creates highly realistic and detailed representations of real construction sites while giving users a sense of immersion. These features enable 360° panoramic augmented reality to become a strong tool for developing training materials specifically for construction safety. In such an environment, construction workers and professionals are capable of navigating within the data-rich environment of a real construction project to observe and identify the safety challenges present in various circumstances. This paper describes the technical development of a virtual safety training environment using a panoramic augmented reality technique. The process of generating the panoramic experience, the techniques for augmenting the captured panoramas with different layers of safety-related information, and the resulting safety training platform are explained in detail. Additionally, this paper presents a pilot study to illustrate the hazard identification training within the proposed platform.

Construction is known as one of the most stressful occupations due to its involvement with physiologically and psychologically demanding tasks performed in a hazardous work environment. Because workers’ stress is a critical factor that adversely affects workers’ productivity, safety, well-being, and work quality, understanding workers’ stress should take precedence in the management of excessive stress. Various instruments for subjective measurement towards one’s perceived stress have been used, but such methods rely on imprecise memory and reconstruction of feelings in the past. Recent advancements in wearable Electroencephalography (EEG) devices possess a potential for quantitative measurement of human stress by directly capturing central nervous system activities from stress. However, its capability of measuring field workers’ stress under real occupational stressors remains questionable. This research thus proposes a framework to recognize construction workers’ stress at the field based on their brain activity recorded from a wearable EEG. Specifically, this framework applies a supervised learning algorithm—support vector machine—in detecting workers’ stress while working in different conditions. Workers salivary cortisol levels, which is a stress-related hormone, were used to label the tasks as low or high-stress level. Relevant time and frequency domain features in EEG signals were calculated. Results yielded a high of 71.1% accuracy using SVM in recognizing workers’ stress, which is a very promising result given that stress recognition with an exquisite EEG device in the clinical domain has at most the similar level of accuracy. The results show the potential for recognizing construction workers’ stress at the field by applying machine learning algorithms using workers’ brain waves recorded from a wearable EEG device. This EEG based stress detection approach will help to enhance workplace environment and conditions as well as to improve workers’ health by early detection and mitigation of the factors that cause stress.

Many studies suggest accidents in construction projects mainly relate to unsafe behaviors of construction workers. When construction workers perceive environment hazards wrongly, they take aggressive shortcuts and opportunistic activities to save effort and time. Therefore, their unsafe behavior highly determined by their perceived risks. In this paper, we propose to develop and utilize some vigilance indicators to quantitatively reflect the perceived risks of construction workers. The indicators are formulated based on electroencephalography (EEG) signals, which associated with human brain activities. However, the detected EEG signal is a hybrid signal that caused by brain activities and muscles movements, which yield to unique data patterns. Therefore, we proposed to implement wavelet packet decomposition to investigate and validate the proposed indicators. We conducted an on-site experiment to analyze the EEG signal patterns and benchmarked the results of the proposed indicators with other existing vigilance assessment approaches.

Fall from heights is one of the most lethal incidents in the construction industry. To mitigate the risk of fall hazards, safety managers need to continuously monitor jobsite conditions to identify potential hazardous situations. While, previous studies develop algorithms to automatically analyze a building to detect fall hazards; their application is limited as the real-time data cannot be collected during the dynamic nature of construction processes. One of the emerging technologies that can address this limitation is an unmanned aerial system (UAS). UASs can provide several advantages for safety managers as they can move faster than humans, reach inaccessible areas of jobsites, and can be equipped with video cameras, wireless sensors, radars, or different communication hardware to transfer real-time data. This research study aims to provide a proof of concept of potential application of UASs in developing an automated aerial system to collect, identify, and assess fall hazards in construction projects. The objective of the study is achieved by collecting real-time video feed of the construction jobsite using UASs, generating point cloud data using image/videogrammetric techniques, and developing an algorithm to process spatial point cloud data to identify fall hazards. The algorithm would ultimately search the point cloud data to detect location of the current guardrails and openings and then checks if they are safety-approved. This paper proposes a workflow for identifying fall hazard using unmanned aerial systems and later will present how some parts of the workflow was implemented and tested in a pilot study.

A fall on the same level is a leading causes of non-fatal injuries among construction workers. Previous research reveals that such incidents are associated with slip, trip and loss of balance (STL) events often caused by unsafe site conditions (e.g., slippery floors, obstacles on the path and uneven surfaces). Consequently, detecting STL events enable site management to identify these hazards and employ suitable risk mitigation “control” measures. This research examined foot plantar pressure distribution for automated detection and classification of STL events using wearable insole pressure sensors. Three volunteers participated in a laboratory controlled simulated experiment that examined different types of STL events, while the corresponding foot plantar pressure data were collected from wearable insole pressure sensors. Diverse features (e.g., time- and frequency-domains, and spatial-temporal features) were extracted from the foot plantar pressure distribution data, which was used to associate different pressure patterns with each type of STL event. Four machine learning classifiers [i.e., artificial neural network (ANN), decision tree (DT), K-nearest neighbor (KNN), and support vector machine (SVM)] were evaluated to select the best classifier. Cross validation results revealed that at approximately 85% of classification accuracy, the KNN classifier achieved the most accurate result using 0.64s window size, indicating a great potential to use the proposed approach to automate fall risk detection. Overall, this method would allow construction managers to understand how workers react to unsafe conditions associated with STL events, so as to minimize the fundamental causes of STL events and thus to reduce non-fatal fall injuries in construction.

One of the major challenges that the construction industry has struggled with is proximity-related accidents. Proximity-hazardous situations involving the ground worker and construction equipment often occur in roadway work zones because of their complex and dynamic interactions in proximity. To detect potential hazardous situations, many researchers have explored various sensing technologies and approaches to address this challenge. Most of their research has focused on identifying the capability of developed/used systems in certain settings. Recent studies found that various interactions (e.g., equipment with various speeds) could cause an alert delay of certain proximity sensing and alert systems. Despite this, limited research efforts have been made to explore this problem using sensing technologies. To handle the issue with the potential delay, which can lead to injuries or fatalities, this study introduces adaptive signal processing using bluetooth low energy (BLE) sensors. The adaptive signal-processing approach demonstrates that the responsiveness of the BLE-based proximity sensing system concerning delay can be improved.

Existing bridge guardrails generally serve as an effective barrier to protect workers from falls to lower levels during maintenance work on bridge-decks. However, a large number of bridge guardrails do not fulfill the regulatory height requirement of 42 ± 3 in. for sufficient fall protection. One underutilized but effective solution is to install fall protection supplementary devices (FPSDs) on existing bridge guardrails to increase the overall barrier height while work is being performed. Yet, workers experience significant challenges when attempting to use these devices. First, not all FPSDs available in the market are compatible with every bridge guardrail. Second, workers currently use an inefficient trial-and-error based procedure to test the compatibility between guardrails and FPSD systems in the field. Third, when compatible FPSD systems are not readily available, workers often use less effective fall protection controls that expose workers to higher levels of safety risk. The objective of this study was to find suitable FPSDs compatible with bridge guardrails in North Carolina. To overcome the issues associated with the manual procedure, we used virtual prototyping methods to (1) capture the design characteristics of bridge guardrails and FPSDs, and (2) complete compatibility testing by attempting to attach each FPSD onto each guardrail in a virtual environment. To offer corroborative evidence of the proposed methodology, a case study is presented evaluating the compatibility of commercially available FPSDs with prevailing guardrails in the state of North Carolina. The implementation of this virtual compatibility testing method will allow practitioners to efficiently and cost-effectively select compatible FPSDs to improve safety, efficiency, and productivity. It is anticipated that the advantages of this method over manual techniques will encourage more safety practitioners to adopt FPSD systems to protect their workforce.

Situational awareness concept has been long used in aviation and medical fields to understand and improve an individual’s ability to interact and comprehend complex dynamic environments. This study attempts to empirically test situational awareness model proposed by Endsley within construction domain. Specifically, we will test how different levels of situational awareness interact with each other and how they interact with decisions. To achieve this goal, a controlled experiment was designed and conducted where the investigators (1) induced positive, negative, or neutral emotions in 66 subjects; (2) exposed participants to construction hazards within a high fidelity virtual environment; and (3) measured participant’s hazard recognition skills, their understanding of those hazards, severity assessment, and subsequent decisions. The results revealed there is moderate positive correlation among each level of situational awareness. Linear mixed-effects model analysis revealed that the three levels of SA positively predicted each other. Also, the analysis revealed that there were differences in the degree of influence from each level of situational awareness on the final decision of an individual.

The construction industry experiences the greatest proportion of workplace injuries globally. Many efforts have gone to improve the effectiveness of safety training using advanced gaming technology and virtual reality (VR) technology. Most existing serious gaming programs are scenario-based which provide trainees, e.g., construction workers, an immersive virtual environment where they may process the construction tasks at the job site. However, the training scenarios are often determined by the developer or designer’s judgment. This is problematic since some important accident-associated details and elements are ignored during the scenario design. To address this problem, this work presents an approach that enables to automatically identify design elements from real world accident reports. The accident reports (n = 232) used in this study are gained from the National Institute of Occupational Safety and Health (NIOSH) for a period of 27 years (1989–2015). The approach integrates human factors engineering and machine learning techniques to gaming technology. This work also provides a case study that adopts the presented method in developing safety training scenarios using BIM and virtual reality headset for home builders. The case demonstrates improved accuracy and efficiency for the scenario design and storybook which are critical to the success of a VR-based training program.

In construction, hazard identification is a critical first step in safety management. To complement current hazard identification practices that rely on manual inspection, our previous study proposed an approach to identifying hazards by analyzing workers’ collective behavioral responses—particularly the collective abnormality of workers’ gait patterns as observed via foot-worn inertial measurement units. Alternatively, workers’ moving trajectories represent behavioral responses that may contain important information about site conditions, since workers tend to change their walking path to avoid the risks posed by hazards. In this context, this paper presents an approach to quantitatively assess the level of divergence within workers’ collective moving trajectories. The proposed approach first defines a representative trajectory from the quadratic polynomial regression–fitting of a given dataset and then assesses how divergent a moving trajectory is by quantifying the dissimilarity between each worker’s moving trajectory and its representative trajectory. The results from a laboratory experiment simulating bricklaying activities demonstrates that such measurements of divergent trajectories can indicate the location of hazards.

Robust hazard recognition is critical to reducing the estimated 1,000 fatalities and 230,000 injuries in the U.S. construction industry each year. By adopting methods from neuroscience, this research develops a more accurate method to measure and differentiate cognitive response during hazard recognition tasks. The hypothesis is that the temporal response and cognitive load are influenced by hazard energy source and severity. In a pilot study, engineering students (n = 14) were shown 24 images total in varying (low to high) severity and hazard type (i.e., gradational, mechanical, electrical, and chemical). The results find that the time to peak cognitive activation (i.e., response time) increases as hazard severity decreases. The type of hazardous energy (gravitational, electrical, mechanical, and chemical) also significantly influences the time to peak response. Participants were found to respond more quickly to gravitational hazards while most slowly to chemical hazards. The cognitive energy (i.e., peak or mean cognitive effort) associated with specific regions in the brain such as attention, working memory, topographic memory, and emotional stimuli varied significantly based on hazard type. Most notably, images with severity of gravitational hazards produced a significant increase in activation in the part of the brain associated with topographic memory and motor initiation. Unexpectedly, high severity chemical hazards, were observed to have higher mean activation in the area of the brain associated with unpleasant emotion processing and odor detection. These results serve as a proof of concept that both hazard severity and type are processed using distinctly different regions of the brain. Measuring cognitive demand and localization of brain activation can provide more detailed understanding of the hazard recognition time and neural processing patterns. Future research can begin to test interventions, such as training and mnemonics, to increase hazard recognition response time, attention, and working memory.

Noise induced hearing loss (NIHL) has been a pervasive problem for all construction trades, yet construction workers still lack sufficient knowledge concerning hazardous occupational noise. Due to the nature of invisibility itself, noise exposure is hard to be intuitively perceived through traditional training methods to improve workers’ perception of noise hazards, such as, short verbal and written instructions, during employee orientation and site safety meeting. With an increasing adoption of advanced information technology in construction industry, building information modeling (BIM) visualization-enhanced safety training has been studied to raise occupational safety awareness in an intuition way. During training process, trainees’ level of attentiveness throughout instruction is generally associated with their learning efficacy. However, little study has been done to quantitatively evaluate the level of attentiveness during BIM-enhanced safety training for occupational awareness improvement. Therefore, this research proposed a quantitative method to validate the level of attentiveness using BIM-enhanced noise exposure training to enhance workers’ noise awareness through monitoring their neuropsychological activities. The brain activation patterns of the test subjects were collected and compared between the traditional text-based and auditory-based, and the BIM-enhanced training. The preliminary results illustrated that the BIM-enhanced training yielded higher level of attentiveness than other training methods, which could successfully prove its capability of promoting workers’ awareness of workspace noise hazard.

The design for safety concept has gained traction in the construction industry as being a viable method for removing safety hazards. Much research has used subjective data to support arguments for the concept; however, the concept has limited support from empirical research. In an effort to better understand how hazards are identified in design, this research explores the extent to which different forms of design information (e.g., three-dimensional building information models and two-dimensional computer aided designs) affect hazard identification capability. To do this, 7 construction designers and 11 construction supervisors from Colorado and North Carolina were asked to analyze real design information and identify all associated downstream construction safety hazards. The associated hazards for each work activity were known based upon previously validated research. The forms of design information included three-dimensional building information models, two-dimensional computer aided design drawings, and a combination of the two. The preliminary results suggest that empirical research can be performed to validate the design for safety concept and early results suggest that three-dimensional information is superior to two-dimensional with respect to safety hazard recognition. Further empirical and experimental research is needed to explore this topic.

This paper presents a new method for detailed activity analysis of dynamic construction resources in highly varying videos obtained from construction site cameras. Toward this goal, we propose a Hidden Markov Model (HMM) that is able to automatically discover and assign sequences of activities that are most discriminative for an observed construction operation. To do so, the algorithm leverages dense trajectory features from a detected dynamic resource (e.g., excavator) in a video. Using these dense trajectory features, we train a Gaussian mixture model (GMM) to estimate the probability density function of each activity with multiple one-versus-all support vector machine classifiers. The proposed HMM also models duration of each activity, and the transition between activities (e.g., “swing bucket loaded” after “load bucket” for earth-moving activities of an excavator). As a proof-of-concept, we train and test our HMM+GMM model on an unprecedented dataset of 10 real-world long video sequences of interacting pairs of excavators and dumptrucks. Our preliminary experimental results on long-sequence activity recognition in presence of noise, occlusions, and scene clutter demonstrate the effectiveness of our method.

The construction industry is a high-risk industry in terms of job site safety. Construction workers’ unsafe behaviors has been criticized as a key driving factor of construction safety incidents. A variety of studies indicate that social influence greatly affects workers’ safety behaviors. However, the social influence on workers’ safety behaviors in hazardous situations has not been well explored, partially due to the difficulty of simulating hazardous situations in the real world. To overcome this limitation, this research utilized a multi-user virtual reality (MVR) system with motion tracking function to simulate hazardous scenarios to study how social influence affects construction workers’ safety behaviors. A hazardous virtual environment with a plank between two high-rise buildings was created to simulate the high-rise walking scenarios of iron workers in construction projects. A human-subject experiment (n = 126) was conducted to investigate the role of social influence in a simulated scenario involving walking across a narrow plank between two virtual high-rise buildings. The participants walked through the plank in the virtual environment while observing different scenarios, such as seeing a person slowly walking across the plank or falling from the plank. Participants’ behaviors, such as the time taken to walk across the plank, were recorded and analyzed to understand effects of social influence. The results of the experiment indicate that social influence played an important role in safety behaviors in the virtual hazardous situation. In addition, the developed MVR system is able to serve similar experiment in the future.

Mobile cranes, as an integral component in lifting operations, are widely used in the construction industry all over the world. Unfortunately, mobile cranes are also associated with a large proportion of construction fatalities. Despite the complexity and dynamic nature of crane operations, a clear understanding of crane failure modes and their contributing factors is critical to mitigating crane-related hazards. Failure mode analysis has the potential to detect and eliminate risks proactively and thus prevent accidents from happening. This paper explores the deployment of two classic analytical approaches, failure mode and effect analysis (FMEA) and fault tree analysis (FTA) in the context of mobile crane failure analysis. Based on the results of a case study, a comparative analysis was conducted to reveal the merits and limitations of each analytic tool. It was found that using analytical tools in mobile crane failure mode analysis provides better understanding of the impact of uncertainties on crane life and failure probabilities. Leveraging failure analysis tools helps crane manufacturers, owners, and operators to make risk–informed decisions in the design, maintenance, and operation of mobile cranes.

Hazard identification is one of the primary steps in effective safety management. However, research has shown that a large proportion of construction hazards remain unrecognized in workplaces, which expose workers to unanticipated risks. While researchers and practitioners have developed several training programs to improve hazard recognition levels, much of this effort has proceeded with limited understanding of factors that impede hazard recognition at the worker level. Recent studies have suggested a strong correlation between viewing patterns of workers and their hazard recognition performance. Hence, it is important to study and analyze the viewing patterns of workers to gain a better understanding of their hazard recognition performance. From the training point of view, scan paths and attention maps can be used, very effectively, to provide personalized and focused feedback to workers to communicate search process deficiency, trigger self-reflection processes, and improve subsequent hazard search performance. However, providing the personalized visual attention feedback requires the recording of scan paths of individual workers and examining the data manually, which is very time-consuming and susceptible to human errors. Consequently, the manual process can result in inaccurate assessments and inefficient feedback. In an attempt to automate and scale up the generation of personalized feedback, this paper proposes a computer vision-based method that tracks workers in a construction site and automatically locates their fixation points (collected by a wearable eye tracker). Using the localized positions of workers and the fixation data from the wearable eye tracker, each worker’s viewing behavior (i.e., attention distribution, fixation points, etc.) is monitored analyzed to generate personalized feedback for training purposes. The presented case study validates the proposed method and the results show the potential of automating and scaling up personalized feedback generation.

The majority of routing decision approaches in emergency response operations aim at fire emergency in buildings and normally focus on building operation and maintenance phase. However, in the construction industry, falls are the most frequently occurring types of accidents resulting in fatalities, and there exists limited research considering potential fall risk on working routes where construction workers perform tasks such as material handling at the construction stage. This research contributes to providing an approach to consider potential fall risk on working routes and suggest one route with relatively lower risk by integrating BIM, Bayesian belief network and ant colony algorithm. Building information modeling can retrieve building geometry, integrate information from surrounding environment and thus help identify and evaluate potential risk at the construction stage. Based on the geometry information retrieved from the BIM model, Bayesian belief network is applied to assess potential fall risk of different identified fall scenarios. The obtained data after Bayesian belief network analysis is input into ant colony algorithm to plan safe working routes on a typical construction site. An example is presented to demonstrate the simulation result.

Many construction accidents result from workers’ unsafe behaviors, and evidence demonstrates that individuals who consistently engage in risky behaviors often have a high rate of accidents. These risk-prone workers’ biased evaluation of hazards (risk perception) and high tendency to take risks (risk propensity) leave them insufficiently prepared when encountering hazards that may cause accidents. Therefore, measuring risk perception and propensity and using these measurements to identify risk-prone workers can provide opportunities to effectively interrupt unsafe behaviors. Our previous study indicated that subjects tend to change their gait patterns before encountering a hazard, a result that may occur due to the subject’s perception about the risks of the hazard. This study examines subjects’ gait adaptations when the subject encounters the same hazards repeatedly to determine whether gait adaptations depict changes in the subjects’ perceived risk from the hazards over time. The results from an experiment confirmed the hypothesis that the subject’s gait adaptation upon encountering a hazard decreases as he/she repeatedly encounters the same hazard. This result is in accordance with the theory of “outcome feedback” and represents an opportunity to use gait adaptation as a quantifiable behavioral response indicator for identifying risk-prone workers.

Much of the construction work that occurs on high-speed roadways takes place at night to minimize impacts on drivers. Performing work at night exposes workers to hazards that are not present during the daytime such as driver drowsiness, alcohol use, and reduction in visibility. Illuminating the area where work is taking place can provide sufficient lighting for the workers to see their work and help them to be more visible. Depending on various factors such as the amount of light needed and the nature of work operation, different lighting equipment is used for illumination. Mobile lights attached to the construction equipment, portable lights, and personal lights worn by workers are examples that can be used to provide the necessary lighting. The authors performed a survey of current lighting systems and practices in preservation project work zones across the U.S. The impact of two lighting systems including the light tower and balloon light on visibility of the workers was evaluated. The authors examined the visibility of workers at different distances, various locations in regards to the light and with different high visibility apparel. The results of the survey showed that a portable light tower and balloon lights are the most commonly-used types of lighting system in construction work zones. Moreover, workers feel safer when using additional personal light equipment such as a flashlight. The location of the worker on the roadway relative to the light location is a significant factor in the visibility of the worker. Selected lighting strategies are provided to be considered in designing nighttime work zones to improve the visibility of workers and increase safety in highway work zone construction.

Construction firms invest millions of dollars toward safety training to improve safety performance. Despite the efforts, only 10–15% of training investments convert to desirable outcomes. In particular, training for production skills often take precedence over training for safety procedures. Further, many training programs lack an assessment of competency, requiring trainees to demonstrate acquired knowledge. The purpose of this research is to develop and test a model of training and competency programs for construction contractors. The training elements of the model are based on a literature review and discussions with subject matter experts. To evaluate the importance of elements, safety professionals were surveyed to determine the degree of significant contribution to a training and competence model. Following the model development, a questionnaire survey was distributed to project personnel to test its relationship to actual safety performance on the project. A total of 45 respondents from 10 projects across the United States participated in the data collection. Strong statistical significance was found between the level of the personnel training programs and the safety performance of the project. The primary contribution of this research to the body of knowledge is to model training and competence programs and demonstrate its relationship to project safety performance.

Subcontractors have significant roles on construction projects, and thus, are influential to the project’s cost, schedule, quality, and safety outcomes. Safety issues occasionally arise with subcontractors that could be prevented if there is a good relationship between subcontractor and the other parties involved in a project. The purpose of this research was to develop a subcontractor management assessment program for contractors and owners to assess their subcontractor management safety policies. Through a literature review and guidance from industry experts, elements involved in subcontractor management were identified. An extensive questionnaire survey assisted in validating its contents. After developing the rating tool, ongoing projects assessed their subcontractor safety management policies. Thirty project management personnel and 15 site level personnel participated in this study across 10 projects. The primary contribution of this research to the body of knowledge is to identify a rating tool and evaluate its relationship to safety performance.

The construction industry is among the most dangerous industries, as one in five worker deaths is related to construction job sites. Construction projects are unique in nature with different rationales and requirements. There are safety issues within each step of the construction phase at each project. This study aims to include factors of uncertainty such as performance of construction workers and their state of alertness in their work conditions in response to external stimuli. To address the relationship between human factors and safety concerns at job sites, an exploratory study was conducted to evaluate which factors impact a worker’s perception of the jobsite and how these factors can be used to predict and decrease jobsite accidents. This study is a multidisciplinary project and combines three different domains including construction management, construction safety, and cognitive psychology, to evaluate workers’ perceptions of safety hazards based on their mental alertness. This study is proposed as a pilot study and will pave the way for the further implementation of mental alertness measures at construction projects.

A recent study has shown a positive correlation between owner satisfaction during construction projects and the contractor’s level of project risk management (PRM) capability. This capability can be measured and is known as risk maturity. Risk maturity can be improved within individuals and project teams. Both training and tools are available for improving risk maturity, however, previous studies have found that there are consistent barriers to implementing PRM. This paper provides a review of the barriers and identified five common PRM barriers. Risk maturity scores were captured for 25 contractors, additionally, the contractors were asked to rank order the five common barriers. This study measured the association and correlation between the rankings of the barriers and the risk maturity levels. Data is captured through surveys of electrical construction companies through a research grant with ELECTRI International. The associations between the barriers and risk maturity levels are identified with a Pearson correlation test and the correlation is tested through a linear regression analysis. Eight correlations were found between the levels of risk maturity and the barriers to implementing PRM. The findings from this research will help professionals and organizations identify and plan for the barriers to implementing PRM dependent on their risk maturity level.

Workers’ safety behavior is viewed as a critical determinant of workplace safety in construction industry. Social cognitive theory points that individual’s behavior is affected by the role of psychological perception which to some extent, depends on the individual characteristics. The current study investigated the effect of psychological capital (PsyCap) on construction workers’ safety behavior through the mediation of safety motivation using survey data collected from 352 respondents who worked in construction industry in China. The empirical results revealed that workers’ PsyCap had a direct and positive influence on safety compliance and safety participation, and the effects of PsyCap on safety compliance and safety participation were both partially mediated by workers’ safety motivation. Regarding to sub-dimensions of PsyCap, we found that (1) four sub-dimensions of PsyCap all had positive effects on safety compliance and safety participation, and (2) these four relationships were all partially mediated by workers’ safety motivation. The findings urge construction site managers to consider workers’ PsyCap and its role in motivating workers to perform safely, so as to improve workplace safety.

Falls are a leading cause of fatal and nonfatal injuries in construction. One of the most important steps toward preventing falls is to identify and measure the factors that can affect the construction workers’ fall risk. While several intrinsic and extrinsic factors can affect workers’ fall risk—such as the effects of aging workers, job site environments, walking habits, workers’ experience, and workers’ equipment—one unexpected factor that can increase fall risk is the incorrect use of personal protective equipment—including safety harnesses—since when these safety tools are misapplied, they cause instability in the workers’ body. The objective of this study was to assess the effects of the formation of tools connected to construction workers’ full body harnesses on the fall risk of construction workers. Using the time-series quantitative kinematic measures obtained from inertial measurement units (IMUs) connected to the workers’ waistline, the postural stability of a group of subjects was measured by calculating the velocity of Center of Pressure (COPv) and the resultant Accelerometer (rAcc)—lower rAcc and COPv values mean lower fall risk for construction workers. The postural stability for each worker was calculated for two different postures (standing and squatting) and three different formations of the tools attached to the full-body harness. The t-tests’ results in the mean values of the calculated rAcc and COPv showed significant differences in the postural stability of subjects with different formations of tools connected to the full body harness. When tools were not connected, workers had the lowest rAcc and COPv values; asymmetric loading formations’ rAcc and COPv had higher values than symmetric loading formations. The higher risk caused by asymmetric connected-tools formation express the importance of tools attaching formation to construction workers’ safety.

The construction industry continues to be plagued by high injury and fatality rates compared to other sectors. The occurrence of occupational injuries and fatalities are greatly influenced by worker risk-taking, unsafe work behaviors, and work environment. This exploratory study analyzed a total of 315 responses collected in Colorado (n = 218) and Puerto Rico (n = 97) using the safety culture and risk perception survey instrument. Responses were graded using a Likert scale 1 to 5 where, 1 = highly disagree and 5 = highly agree. The Puerto Rico group scored higher overall for a positive safety climate with a total score of 118 versus 115 for Latino construction workers in Colorado. Nearly half the workers were 30 years of age or younger. Findings revealed that Colorado Latino construction workers reported a higher level of concern for job related risks with a mean score of 3.9 compared to the Puerto Rico construction workers with a mean score of 3.5., p-value 0.05. The workers in Puerto Rico also reported that their supervisors were better informed about safety issues compared to the Colorado cohort with a mean scores of 4.7 and 4.5 respectively, p-value 0.04. The Colorado Latino workers indicated that management places most of the blame for accidents on the injured employee with a mean score of 3.2 compared to the Puerto Rico workers with a mean score of 2.8, p-value 0.01. This study contributes to the body of knowledge by increasing the understanding of perceptions that may contribute to factors influencing decision making, unsafe work behaviors, and resulting work related injuries or fatalities.

Leadership is proven as a key factor impacting safety while researchers and practitioners are fostering proactive approaches to preventing workplace injuries. Currently, safety leadership and its impact on safety performance is gaining importance within the construction management academia. However, it is still a crucial yet unsettled research issue that how to be an effective safety leader in the construction industry, and how to assess leadership effectiveness. Safety leadership effectiveness is highly context-dependent, requiring researchers and practitioners to fully consider the characteristics of the construction industry and project-based organizations when assessing and improving safety leadership. Based on an empirical study, this paper aims to propose safety leadership effectiveness assessment criteria, which consists of several key effectiveness indicators of safety leadership for both project owners and contractors. A preliminary indicator system was extracted from the current literature. A questionnaire survey was designed to further identify key indicators, and a total of eleven construction projects were involved. By using a fuzzy statistical analysis method, this study determined five key effectiveness indicators for owner safety leadership and six key indicators for contractor safety leadership. This paper contributes to the current leadership research by proposing the assessment method and an indicator system for safety leadership effectiveness in the construction industry, thus serving as important tools for safety leadership benchmarking between different enterprises and projects. The method and indicators can help construction practitioners determine whether their measures and actions will take effects when they plan to improve safety leadership of project management teams. Conclusions also give insights into the major criteria against which project managers of owners or contractors should behave as effective safety leaders, in order to improve safety performance of construction projects.

The construction industry must plan, manage, and mitigate the safety risk that is passed down to the contractor from the owner and/or designer. Such risk is often referred to as residual risk. After the project managers and safety managers identify the safety hazards, assess their impact, and design a safety plan that addresses the hazards, the on-site personnel (foremen, crew leaders, laborers, etc.) will eventually face the risk and execute the project accordingly. This process, however does address the construction worker’s assessment of safety risk for their fellow workers. Will the process of assigning tasks and/or executing tasks lead to another type or amount of residual risk that is being assumed or passed on through the chain of command? Previous research has compared the safety risk perception of construction workers and safety experts. However, an unanswered question is, how do construction personnel perceive the safety risk associated with the work at hand for workers other than themselves who may have more or less training and experience? To investigate this question, the researchers conducted a survey of more than 200 construction workers from different states. The analysis of the survey responses shows that construction workers gave different values of safety risk associated with the same task when they were asked to assess the level of risk to themselves, to their fellow workers with less training and experience, and to those who have more training and experience. Furthermore, the primary theory that comparative risk is associated with the perceived control was found to be unsuitable for construction.

Leading safety indicators assess construction behaviors and processes prior to the occurrence of safety breaches. These have emerged as a more effective alternative to traditional safety measurements in the construction industry. While exiting research has extensively defined these indicators and their correlation to safety performance, there is still insufficient understanding of their penetration on local and regional levels. This research aims to explore, define, evaluate, and provide guidance in relation to utilizing leading safety indicators in Tennessee. The interdependent research methodology incorporates (1) developing an industry questionnaire based on a comprehensive literature review, (2) disseminating the questionnaire to construction professionals in Tennessee, (3) analysing the results through a combination of descriptive and bivariate statistical analysis techniques, and finally (4) interpreting the results and drawing conclusions and recommendations. The survey results showed that over two thirds of the firms investigated used instituted systems of leading indicators. Yet, firms that had no knowledge of the concept or did not use it, still applied a wide range of practices similar to leading indicators. The most popular indicators amongst the studied firms were related to simple safety tasks and routine practices such as housekeeping, use of personal protective equipment (PPEs), and substance abuse programs. Conversely, more complex indicators related to strategic and policymaking programs, such as contractual safety obligations and evaluation of safety meetings and reporting systems, were less popular. Such complex practices were more likely to be used by larger companies over smaller ones. Following these findings of this research, and by repeating similar studies on other local and regional areas, more effective use of leading indicators can be achieved with greater understanding of current safety systems, contributing eventually to reaching a zero-accident construction industry.

Communication plays a vital role in risk identification and accident prevention in construction workplaces. Current practices that rely on modes such as phone calls and video conferencing do not facilitate instance access to information, context-based perception, and visual interaction that are essential for effective communication in modern construction workplaces. This research attempts to evaluate the feasibility of applying an emerging mixed-reality (MR) technology in ameliorating safety risk communication at construction jobsites. A holographic application that runs on Microsoft HoloLens to enable visual interaction and remote collaboration was developed. This was followed by an assessment on the holographic application through trials and feedback from practitioners in the construction industry. As an ongoing study, this paper focused on and reported assessing the accuracy of applying MR in site risk communication. It was benchmarked against the conventional communication methods namely, phone calls, walking up to people and talk, video conferencing, and emails. The analysis of the collected survey data attested the performance as well as signified the feasibility of applying the MR technology to enhancing risk communication and hazard identification for better safety management at construction jobsites.

Interest in construction innovation has grown considerably over the past three decades. The growth in innovation research was largely spurred by the need to improve poor project performance within the construction industry. A primary contributor to poor construction industry performance is the high rate of construction worker injuries and fatalities. Past studies indicate that adoption of new technologies—including safety related technologies—in the construction industry is subject to resistance from construction stakeholders. Although studies focused on technology adoption drivers and barriers in construction exist, there have been limited studies focused on determining the factors that drive the adoption of safety technology. Using technology acceptance theories, a technology centered change model, and a value proposition framework, the present study proposes a theoretical framework that could assist in improving the odds of safety technology adoption. First, an extensive literature review spanning the construction industry and other adjacent industries was conducted to identify the factors that influence adoption of safety technologies. Based on the results of the literature review, 12 factors were identified. Results from a survey questionnaire indicate that cost savings, technology effectiveness, reliability, ease of use, and useful technical attributes are the key factors that influence adoption decision. This finding informs the development of a theoretical framework—construction safety technology adoption framework (C-STAF). The study results have the potential to create practical directions for technology manufacturers and end-users thereby improving the adoption of safety technologies in the construction industry.

Hazard recognition is an essential element for successful accident and injury prevention. However, studies have revealed that construction workers fail to identify a large proportion of hazards in their workplaces. Therefore, understanding factors that adversely affect hazard recognition performance is a fundamental step towards improving safety performance. Given the unique, complex, and dynamic nature of construction operations, past research has provided anecdotal evidence suggesting that distraction may be correlated to undesirable safety outcomes such as injuries. For example, Hinze’s distraction theory suggests that workers are more likely to be involved in an accident while they are distracted. However, these theoretical propositions have not been empirically tested. To address this knowledge gap, the objective of this research was to test the hypothesis that distracted workers will identify fewer hazards than undistracted workers. To test the hypothesis, an experiment was conducted with 70 construction workers where the participants were randomly assigned to a distracted or an undistracted group. Sixteen pre-selected case images, representing real construction operations, were used to evaluate the hazard recognition performance of the workers. For the distracted group, distraction was induced using visual stimuli presented as unrelated video clips during the hazard recognition activity. The data analysis results showed that the distracted workers recognized a smaller proportion of hazards than the undistracted workers (p-value = 0.003). The research findings indicate that reducing workplace distractions can be a useful intervention to improve the safety performance of construction workers such as hazard recognition levels.

Previous research indicates that designing for construction worker safety (DfCWS) is an effective method to reduce worker exposure to construction hazards. DfCWS requires that designers play a particular role, and pay specific attention, with respect to worker safety when making design decisions about the permanent features of a facility. The primary objective of the present study is to examine the frequency of incorporating worker safety when designing a facility with reference to other critical design criteria, such as quality of design and cost of construction, in order to determine the level of priority given to worker safety during the design process. Previous research, based on anecdotal evidence, found that worker safety during the design process may not be given a level of priority that is equal to that given to other design criteria. However, no empirical evidence was provided. The present research aims to bridge this knowledge gap by providing statistical evidence of the level of frequency of incorporating worker safety in the design process. A survey questionnaire was used as the primary data collection method for this study. Statistical analysis of 101 responses from design professionals distributed throughout the United States (U.S.) revealed statistically significant evidence that the mean frequency ranking of worker safety is less than the mean frequency ranking of other design criteria (design quality, construction cost, owner demand, building aesthetics, construction schedule, and end-user safety). This finding indicates that workplace safety typically receives the lowest level of priority during facility design compared with other design criteria. The apparent lack of safety training and knowledge observed among the study sample may be a key reason behind the lack of priority given to workplace safety.

Previous studies on designing for construction worker safety were founded in part on theory, derived primarily from the time-safety influence curve introduced by Szymberski in 1997, that the ability to mitigate construction safety risks is greater during early stages of facility development, as opposed to after the start of construction operations. It is concerning that the time-safety influence curve has not been validated using empirical evidence, and the relationship between time and safety was primarily established based on anecdotal evidence, including subjective judgment using personal experience. The present study attempts to address this concern by examining the validity of the time-safety influence curve using empirical data and statistical analysis. Injury and safety data were collected from 64 construction projects in the United States (U.S.) using the sustainable construction safety and health (SCSH) rating system, a tool designed to measure and evaluate the extent to which workplace safety and health is given priority during different phases of facility development. The result from the statistical analysis revealed strong evidence of correlation between timing of safety input and project safety outcomes, such as rate of incidents. This finding validates the theory behind the time-safety influence curve and provides statistical as well as empirical evidence that timing of safety input is associated with different incident rates. The present study is expected to help motivate construction stakeholders to address construction safety earlier in a project’s lifecycle to effectively achieve desirable safety outcomes.

Fall hazard in the construction industry has been recognized as a leading factor endangering the workers’ safety. The loss-of-balance is one of the root causes responsible for fall incidents. In this review, the attention is focused on studies on the workers’ fall from human balance control point of view. Status quo was reviewed to provide a comprehensive picture of falls due to loss-of-balance. The central questions are: (1) what is the mechanism of human balance control for static and dynamic postures and the sources of influence? and (2) what variables and methodologies of balance measurements are used for construction population? The validity and the pros and cons of the reviewed measurements are compared and discussed. Research gaps in environmental, task-related, personal factors, and contextual balance measurements are discussed at the end of the paper.

A worker’s attentional and cognitive failures—such as lack of attention, failure to identify a tripping hazard, or misperception about a hazard’s risks—can lead to unsafe behaviors and, consequently, accidents. Previous literature has shown that individual characteristics such as personality may affect human’s selective attention. However, few studies have attempted to empirically examine how a worker’s personality affects attention and situation awareness on a jobsite. The present study examines how workers’ emotional stability (neuroticism) affects their cognitive failures (especially attentional failure) when they are exposed to fall-to-same-level hazardous conditions. To achieve this goal—and given that eye movements represent the most direct manifestation of visual attention—the personalities of construction workers were assessed via self-completion questionnaires, and their attention and situation awareness were monitored continuously and in real-time using a mobile wearable eye-tracking apparatus. Correlational analyses revealed the significant relationship between neuroticism and the attentional distribution of workers. These results suggest that workers do not allocate their attention equally to all hazardous areas and these differences in attentional distribution are modulated by personality characteristics (neuroticism). A more detailed investigation of this connection yielded a specific pattern: less neurotic workers periodically look down and scan ahead to obtain feedforward information about tripping hazards, and these individuals remain fully aware of the environment and its associated hazards. The findings of this study suggest the value assessing personality to identify workers who are more likely to be involved in accidents.

Before asking if construction safety is outside the realm of resilience systems, we should ask if accidents and injuries are indicators of resilient systems. In construction, as in all industries, no worker plans for an accident or to get injured. On the contrary, many major construction companies have zero injury safety planning and yet, the industry still has some of highest numbers of injuries and fatalities annually compared to all other industries. Resilience systems employ a systems-based approach to analyze and format responses to efficiently cope with future risks, shocks, and changes to an existing system in a timely manner. Accordingly, a resilience system for construction safety would be able to analyze the types of safety risks that the company deals with on all its projects and draft a potential response for each possible case depending on its impact and frequency. This research aims to aid construction companies in incorporating resilience processes into their safety plan in order to better address two issues: (1) how to respond to an accident or a shock that might impact the system, and (2) to improve the overall flexibility of the safety plan. For each of these goals, two resilience systems are introduced and applied retrospectively to an existing case from the Centers for Disease Control and Prevention (CDC) Fatality Assessment and Control Evaluation (FACE) program.

The majority of human-factor models in construction safety assume that risk-taking behaviors, failure to perceive hazards, or misinterpreting the associated risks of hazards are the main contributing factors in accident occurrences. However, the findings for the link between risk-taking behaviors and risk perception are inconsistent. To address this knowledge gap, the current study focuses on measuring the association between risk perception and the risk-taking behaviors of construction workers. To achieve this objective, 27 undergraduate students from the University of Nebraska–Lincoln with at least 1 year of experience in the construction industry were recruited to participate in an experiment. To measure risk perception, the subjects were asked to assess the risk—in terms of likelihood and severity—associated with various scenario statements related to fall hazards. Subsequently, subjects performed the balloon analogue risk task (BART), a computerized decision-making simulation, to test the subjects’ risk-taking behaviors. The results of a correlational analysis showed that there is a significant negative association between an individual’s risk perception of fall hazards and his/her risk-taking behaviors. Additionally, differences in the risk-taking behaviors of subjects evaluated against their risk-perception scores were examined using a permutation simulation analysis. The results showed that there is a moderately significant difference in the risk-taking behaviors of subjects with low and high fall-risk perception. The research findings provide empirical evidence that people with lower risk perception tend to engage in more risk-taking behaviors. Furthermore, this study is one of the first attempts at using BART in the assessment of risk taking in construction safety and paves the way for a better understanding the human factors that contribute to construction accidents.

Construction projects in China are subject to multiple safety production supervision and liability systems strictly enforced by central or local governments. Such a strong supervision system in practice seems to have limitations in preventing falling accidents that cause life and property losses. Many research studies focused on falling from height (FFH) and put forward many countermeasures, but many safety supervision problems remain. Some example questions are (1) why are the regulatory mechanisms ineffective? and (2) what are the hidden factors that trigger failures of preventing falling accidents? The authors collected 118 official falling accident reports from governmental websites and extracted accident causes from the reports. Based on HFACS (human factor analysis and classification system) and “24 Model” developed in previous studies, the authors proposed an analytical framework for safety supervision assessment and classified accidental causes using the framework. With the data of the reports, the authors completed relative frequency analysis and Chi-square test between the causes of accidents. The authors identified factors that highly correlated with accidents. The main findings include (1) FFH is still the top accident type in terms of frequency and casualties in housing and municipal construction projects in China, (2) “safety devices are not used” is the top-1 cause for FFH, (3) “violation of discipline and directions,” “risky operation,” and “incorrect use of safety devices” which are related to workers behaviors are not influenced significantly by supervision, and (4) supervision company’ safety management has no significant influences on unsafe supervision behaviors related to construction company except “safety education is not in place.”

A proper agenda for hazards with a cumulative impact on the human health is lacking in the construction industry. Poor preventative and compensatory health practices with large deficiencies in employers’ understanding of cumulative health hazards are contributing to this dearth. Profitability, the economic motivation of the construction industry establishes an opportunity for identifying health practices that can potentially mitigate the exposure of its skilled workforce to both short-term and long-term health hazards. Design and planning offer opportunities to both engineers and managers to minimize the exposure of construction workers to cumulative hazards, but such opportunities have not been documented or investigated. In this study, qualitative and quantitative methods are used to determine the practices for the prevention of exposures to welding fumes and crystalline silica. A content analysis of semi-structured interviews with workers and occupational safety and health experts is used to develop survey questions. This study leverages the Delphi method, a structured expert elicitation technique, and uses the survey questions to facilitate the identification and characterize of a comprehensive list of practices spanning planning, design, construction, and work hygiene opportunities/practices. What emerges is that the areas where the greatest opportunity to influence worker health can be at the design and planning stages of a project—potentially creating a new professional paradigm for the construction industry. This research brings to light that ultimately, it is not short-term profitability concerns that prevent the implementation of health practices, but rather it is the need for increasing the understanding within the construction industry of how worker health is directly tied to productivity and profitability in overall business processes.

Use of technology in planning and design purpose is widely known in the construction industry. In this paper, an important issue related to fire safety of schools is addressed. Fire is a potential hazard amongst all that can occur to schools and as a result, require more protective environment. This paper proposes an idea of minimizing the risk of hazardous situations. Using technology for future planning of fire station location will enable us to provide an effective response to school fire hazards in the quickest possible way. To achieve this, GIS-based virtual city model has been introduced with the implementation of data and visualization of this data as graphics. The primary step is to produce a 3D city model using GIS shapefiles. The dataset of interest is related to virtual city model and the data integration process is performed to infuse data into the model. The proposed new tool matches to the model and produces a visually characterized virtual model that displays all data integrated with the model with a great level of detail. The results represent the potential future fire station locations required near the schools, to respond to the hazardous situations very efficiently in terms of time. To achieve this, data from the prominent sources are used to analyze the current situation and presenting the possible improvements using a virtual city model of a case-study. The model helps in understanding the findings of this study and describe the vitals through the model.

Performance-based design has become inherently important in construction. It is absolutely necessary to assess the level of safety provided by the building structure to its occupants in normal conditions and more importantly in fire incidents, since code compliant structures don’t always offer the needed safety for occupants. This can be achieved by combining static data from building codes and regulations with the dynamic data resulting from the interaction between occupants and between occupants and their surrounding environment. This research proposes a framework that uses building information modelling (BIM) and agent-based simulation (ABS) to assess the evacuation performance of buildings. BIM is used to model the geometry of buildings and to visualize the evacuation process. It acts as a platform for ABS to identify potential bottlenecks and points of hazard, based on the data collected regarding agents behavior in emergency situations. In order to utilize the simulation results in an effective manner, they are integrated into a decision support system (DSS) to help in making better decisions. That’s why the simulation results as well as other data are fed to the decision making model. The developed framework is capable to rank and assess different evacuation plans and/or design alternatives depending on several criteria, unlike other systems that evaluate buildings safety using the egress time as the sole criterion. A case study is presented to illustrate the practicality of the proposed system. The building’s spatial characteristics as well as the occupants’ data (e.g., approximate numbers, percentages of the disabled, and behavioral data such as: movement speeds and pre-evacuation times) are identified to study their potential impact on the evacuation. A number of recommendations, concluded from the conducted research and case study, are presented at the end of the paper.

Reaction time of a snow removal project, which is defined as the duration between the time that snow begins accumulating at a road section and the time that snow is plowed, is a project performance indicator that can be used to evaluate the effectiveness of truck allocation strategies. While sensors, such as truck GPS (global positioning system) and weather RWIS (road weather information system), which track working hours and weather conditions, respectively, are used to collect large amounts of data, these data are not fully utilized to optimize reaction times of snow removal projects. In this research, the relationship between truck performance and weather information was analyzed. Sensor data were extracted, clustered, and refined; stochastic truck travelling speed and stochastic plowing speed were then mined and associated with the weather conditions of corresponding road sections. A data-driven, simulation-based optimization approach, which uses this mined data as input, was also developed to minimize reaction time. A practical case study of a project in Alberta, Canada, was conducted to validate and demonstrate the functionality of the proposed approach, which was simulated and optimized using the in-house simulation software, Simphony.NET. The resultant model allows project managers to predict the impact various truck allocation strategies on project time and cost to ensure that maximum project reaction time is minimized.

The recurrent disastrous events impact a broad spectrum of stakeholders. Accordingly, decision-makers need to decrease the vulnerability of the built environment to future hazards to achieve long-term sustainability for the future generations. This paper discusses a decision-making framework via an agent-based model that assimilates the different participating entities in the recovery processes, while decreasing the economic vulnerability of the built environment. The developed model accounts for the objectives of the government agencies, and the needs of the residential and economic sectors in repairing their households and regaining the monthly revenue. The developed model utilizes a learning module that depicts the learning behavior of the stakeholders. In addition, the model utilizes a well-established economic vulnerability indicator that evaluates the susceptibility of the community to economic losses. Through integrating the economic vulnerability indicator into the associated stakeholder’s objective function, effective strategies for post-disaster recovery were identified. The model was tested on the post-Katrina recovery in the three coastal counties of Mississippi. The model was able to provide better recovery, and lower economic vulnerability values in comparison to the existing conditions in the three counties. As such, the proposed decision-making framework can be utilized by the recovery and planning agencies to balance between the short-term redevelopment objectives, and the long-term goals in decreasing the built environment vulnerabilities. Such innovative decision-making framework will be implemented on other case studies to furtherly validate the results of the model.

The frequency and magnitude of natural disasters have increased over the last decades, putting more demand on the local, regional, and national emergency agencies to provide much needed response to affected populations. Temporary housing constitutes a major portion of the post-disaster recovery effort, which take the form of light shelters for immediate housing followed by interim housing that extends up to 18 months before the victims move to their permeant housing. In the U.S., the traditional and main form of interim housing is manufactured housing (MH) units, which are federally-regulated modular homes. As such, this paper presents a systems-level retrospective analysis of the use and deployment of MH units in post-disaster recovery effort in the U.S. The analysis focused on: (1) studying the different interim housing systems, including HUD-code manufactured housing; and (2) examining the supply logistics for procuring, maintaining, deploying, and recovering MH unit. Both quantitative and qualitative analyses were performed to study current practices and systems of MH units in post-disaster recovery, and identify potential improvements that can improve the agility and cost effectiveness of interim housing using MH units.

Recent studies stress the critical role of rapid damage assessment in crisis management to reduce losses in natural disasters. Timely damage-related data collection by traditional empirical inquiry methods (e.g., interview and survey questionnaire) is particularly difficult after natural disasters such as hurricanes and floods. Remote sensing technologies including satellite and unmanned aerial vehicles (UAVs) were popularly employed in previous research in support of damage data collection. Compared with orbit limitation and high operating costs of satellites, UAVs demonstrated its advantages in low operating costs, high operational flexibility, and high spatial resolution of imagery. However, using UAVs alone in data collection for major disasters such as Hurricane Matthew is particularly difficult because the affected area was so extensive. On the other side, previous studies indicate that social media (e.g., Twitter) was widely used as a crowdsourcing platform for citizens’ communication and information sharing during natural disasters. Furthermore, citizens can also communicate information updates related to damage (e.g., burned area of wildfire) in natural disasters. Hence, social media presented its potential in providing timely information to support damage assessment. To conduct a rapid damage assessment, this paper proposed the framework of integration of Twitter and UAVs for damage data collection for Hurricane Matthew in Florida. Firstly, this study used real-time Twitter data to prioritize the affected counties requiring the deployment of UAVs. Secondly, we proposed the employment of UAVs in imagery and video data collection in these affected areas at the city and region level. Lastly, processing techniques for image and video were applied for rapid damage assessment, which can be further geographically represented to support crisis management.

After many recent disaster events, community members, government officials, and others are increasingly using social media, particularly Twitter, to seek and share hazard-related information. In some states, such as Oklahoma, the frequency of earthquakes has risen significantly in the past decade, with many scientists arguing that these earthquakes are induced by injecting vast quantities of salty wastewater, a byproduct of petroleum production, deep underground. Because the cause of increased seismicity is debated publicly, communities impacted by these quakes often face uncertainty when it comes to understanding the reasons behind earthquakes and the subsequent effects on they will have on their lives. As a result, many community members turn to social media to seek and share information on topics ranging from debating the cause of the earthquakes to safety procedures during an earthquake. This paper seeks to understand how community members use social media to grapple with hazards in this uncertain context by examining how Twitter networks change after a significant hazard event: the magnitude 5.8 Pawnee earthquake in 2016. The paper presents a method that uses web-mining techniques to develop the Twitter network of Edmond, Oklahoma, 1 week leading up to the earthquake, and the week following the quake, and uses social network analysis to examine these data. Our results show that posts discussing the earthquakes, and their purported causes, increase after an event. The overall network also becomes more fractured and disconnected, which runs contrary to theory that would suggest a more densely connected community after a disaster. The method developed to create and analyze community networks from Twitter data can be used to better understand this phenomenon, which will help us better understand how networks change over time, how information is spread through community networks following disasters, and how communities may respond to and discuss contentious infrastructure projects.

Signage plays an important role in wayfinding in built environments, especially during emergency situations. However, the traditional light-based single-function emergency signage may lead occupants to a fire blocked route or a crowded exit resulting in a delayed evacuation and even more serious situation during an emergency evacuation. A cyber-physical systems (CPS) approach is expected to improve effective integration of event monitoring, real-time information communication, proactive danger identification, and intelligent guidance. This paper proposed a cyber-physical systems approach as a real-time emergency evacuation signage guidance system to guide occupants towards safe and fast evacuation routes considering fire source location and development trends, and human traffic flow trends in real time. The proposed system includes a fire detection sensor network system monitoring fire source and propagation, an occupant sensor network system evaluating the existing occupant traffic flow, and intelligent exit signage systems showing fire-free and crowd-free route directions. All fire detection sensor units communicate with each other to broadcast the danger location information. The occupant sensor network system detects the traffic flow coming in and going out for each direction, and detects and predicts crowd areas based on the traffic flow trends. The effective integration of computational resources (such as wireless sensors, real-time monitoring, data fusion, and prediction) with the building evacuation process will improve the evacuation performance. The opportunity offered by the adoption of a cyber-physical systems approach to make the evacuation process more intelligent and sustainable is discussed and the key benefits highlighted. The expected results would be the reduction of evacuation time and risk to encounter into fatal hazard areas with the installed intelligent signage guidance system, and eventually would be improvement of building evacuation performance and reduction of human injuries and fatalities.

Over the past decade, the U.S. has experienced more tornadoes than any other country. Once considered low-probability events, today over 1,200 tornadoes form across the country each year. States located within the central U.S. are particularly tornado-prone and referred to as “Tornado Alley” (e.g., Texas, Oklahoma, Kansas, Nebraska, South Dakota, Iowa, Missouri, Illinois, Indiana, and Ohio). Existing single-family residential houses located in Tornado Alley are increasingly exposed to wind damage associated with tornadoes, resulting in approximately one billion dollars of annual losses. To facilitate post-tornado recovery, the construction industry must meet demand for reconstruction of residential houses. Construction capacity, defined here as the maximum building volume a construction industry can supply with available resources, determines how efficiently residential housing is rebuilt following a disaster. However, construction capacity is limited by available construction material and labor resources, such as building material wholesalers and residential general contractors. To better understand how construction capacity hinders or facilitates post-disaster reconstruction, this study addressed: How can state-level pre-disaster construction capacity measurements inform post-disaster rebuilding of single-family residential houses in Tornado Alley? The specific research objectives were to: (1) measure state-level pre-disaster construction capacity as the maximum value of residential construction work that can be performed in a given year, based on material and labor availability; (2) calculate post-disaster tornado losses for single-family residential housing based on FEMA damage inspections; and (3) analyze and compare state-level pre-disaster construction capacity to post-disaster single-family housing damages. The results identify which states located within tornado alley are the most vulnerable to future tornado disasters due to availability of state-level construction industry resources needed to meet demand for residential housing construction.

Timely and effective response in the face of disasters is critical as it provides essential service to support the quality of life. However, the response to disasters is challenging as it is a complex process involving various interconnected organizations, information, resources, and tasks. In order to better understand this complex process and identify effective planning strategies in disaster response planning, a meta-networks analysis approach is proposed and tested in this study. In the proposed meta-network analysis approach, the disaster response systems are conceptualized as meta-networks consisting of four types of nodes (i.e., organizations, information, resources, and tasks) and 10 types of links. The critical nodes for the successful completion of response tasks will be identified using meta-network measures. Based on the identified critical nodes, decision makers can evaluate and adopt planning strategies for building resilience in disaster response systems. The proposed meta-network analysis approach was tested in a case study of 2015 Gorkha Earthquake in Nepal. Critical resources and information for several humanitarian response systems (i.e., water, logistics, and shelter) in Nepal after the earthquake were identified using meta-network analysis and validated using qualitative data collected from in-depth interviews and a focus group. The proposed meta-network analysis approach provides an innovative method to conceptualize and capture different entities and their interdependencies during disaster response. The identification of critical nodes and corresponding planning strategies can help decision makers to better cope with disasters.

Human-made and natural disaster have caused devastating loss of life and property damage, resulting in deaths and a significant homeless population. Under such circumstances, temporary housing is a crucial determinant of survival for the displaced people in urgent need of housing and sanitation facilities. The post-disaster emergency shelter is a solution to deal with the crises and reducing vulnerability. Since environment, economic, and social situations vary throughout the world, the most appropriate shelter materials and methods to be used also differ. Currently, there is a lack of an effective evaluation protocol to determine the most appropriate solution for these emergency situations. The optimal shelter material and method should consider multiple objectives such as durability, ease of assembly, assembly time, initial cost, and maintenance costs. This study develops a novel multi-criteria decision making algorithm using analytic hierarchy process (AHP) to determine the optimum combination of materials and construction methods to be used in the shelter’s wall panel. The conclusion simplifies the shelter material selection process, ultimately enhancing reliability and improving the living conditions of the displaced population in the most cost-effective way. To demonstrate the methodology, this research presents a case study included the available products currently on the market and created a mathematical and logical model.

Disaster management encompasses many activities before, during and after the occurrence of a disaster. While preparedness and mitigation are parts of the pre-disaster stage, reconstruction and recovery are parts of the post-disaster stage. All of the activities and stakeholders involved in these stages aim to diminish the impact of a disaster in people’s normal lives. For example, temporary housing after a disaster enables the return to normal life as soon as possible, which can be translated into a positive social impact. In order to maximize the positive social impact of post-disaster temporary housing, this research aims to identify how innovation can maximize the social gains of post-disaster temporary housing. In relation to this, we studied the fabrication and delivery of emergency housing performed by the NGO Fundación Vivienda after the 2017 fire in Chile. First, we defined the social gains that can be maximized throughout the fabrication and delivery of temporary housing. Then, processes, stakeholders, and relationships that can impact the identified social gains were defined. Subsequently, innovations to maximize the social gains during the fabrication and delivery of post-disaster temporary housing are proposed. Finally, a model to calculate the social impact of these innovations is proposed and validated throughout the implementation of the innovations in the NGO’s temporary housing mill. This paper summarizes our findings and learnings measuring the social impact of innovation during the post-disaster temporary housing stage.

The collapse of an urban bridge resulting from a massive fire causes significant economic damage. Preventing a recurrence of such accidents involving massive fire requires special management regulations and strategies for the fires that break out under bridges. This study introduces several strategies for the fire risk management of materials stored under bridges through the lessons learned from similar cases in South Korea. In addition, this study investigates the optimum fire management method for the space under bridges based on the best practices conducted in Korea. This study also conducted fire simulations for materials temporarily stored under bridges with different heights and volumes of the materials. Based on the results of the fire simulation, this study establishes a required distance between bridges and facilities or materials under bridges. Finally, this study suggests a guideline to establish fire management standards for the materials and facilities under bridges.

The increased construction costs following large-scale natural disasters, also referred to as demand surge, occur due to the escalated demand for labors and materials. Building exterior construction’s labor wages are subject to significant fluctuations following natural disasters; however, there has been no empirical analysis measuring and comparing building exterior labors’ wage vulnerability to the natural disasters. The primary objectives of this research are to: (1) quantify the post-disaster labor wage fluctuations for labors working at building exterior construction sector; and (2) highlight the most vulnerable labor markets at this sector of the construction industry. The county-level data on quarterly average weekly wages were collected for more than 100 counties affected by weather-related disasters (floods, storms, and tornadoes) in the United States from 2007 to 2014. Labor wage fluctuations for each sector of building exterior construction is measured as the seasonally adjusted percent change of labor wages in the event of a weather-related disaster. The results of this study show that among the building exterior contractors, masonry contractors—especially non-residential masonry contractors—face the highest inflation in labor wages. In addition, the non-residential labor markets are, in general, more vulnerable to weather-related disasters than the residential labor markets. Moreover, the results show that, in most cases, building exterior contractors face the highest inflation in wages in two quarters after the event of weather-related disasters. It is expected that the results of this study will help policy makers, insurers, reinsures, homeowners, and building exterior construction firms to better understand the post-disaster wage fluctuations of building exterior contractors.

A current concern in roadway construction is the behavior of drivers in response to the location and frequency of construction and permanent signage prior to and within the construction work zone. A research study was conducted to investigate the impacts and effectiveness of temporary construction signage related to multiple sign characteristics, including location, spacing, type, information presented, distraction created, and visibility. In total, 500 survey questionnaires were distributed through Amazon Mechanical Turk to understand drivers’ preferences of temporary signs in work zones in the U.S. The survey revealed that the majority of drivers prefer dynamic signs to static signs. Except for the posted speed limit, drivers prefer to receive more information about work zones from the temporary signs such as distance and travel time through the work zone. Compared to the presence of construction equipment, workers, and lighting in work zones, temporary signage is the least distracting feature. In addition, fisher-exact tests of independence were used to determine the relationship between driver’s age and vehicle type and their perceptions of sign characteristics. The results suggest that driver’s age has a moderate impact on their perception of spacing between temporary signs, and the vehicle type has a moderate impact on their driving behaviors in a long work zone. The results of the study provide construction contractors with guidance on the placement and type of temporary signs, information presented on signs, and placement of construction equipment to ensure safe site conditions and passage through work zones.

Between 2013 and 2016 in the Province of Ontario, Canada, 739 surveys were collected from 70 residential construction sites, and 342 surveys were collected from 34 heavy civil construction sites. Safety climate and safety performance of these two sectors were compared. Overall, residential respondents reported slightly more safety incidents than heavy civil sites but they had very similar safety climate scores. For both sector respondents, “cut/puncture,” “strains/sprains.” “headache/dizziness,” and “persistent fatigue” are the most frequently experienced physical injuries; “slip/trip/fall on same level,” “exposure to chemicals,” and “overexertion while handling/lifting/carrying” are the most frequently experienced unsafe events. The role of management and supervisor safety commitment in improving safety and the impact of work pressure on physical safety outcomes and job stress are highlighted.

Construction workers are more prone to safety hazards compared with workers of other industries according to reports of the U.S. Bureau of Labor Statistics, and transportation maintenance workers are no exception. The nature of the work for this unique audience exposes them to different hazardous conditions, which increases the probability of injuries. As part of a project to improve safety performance for the Kentucky Transportation Cabinet (KYTC) maintenance workers, this study outlines the development of a pre-task safety talk program. By analyzing safety recordable incidents claims of KYTC maintenance employees for a 10-year period between 2005 and 2015, the top leading reasons for claims were determined. The results show 12 leading reasons that were repetitive among 10 typical operations for KYTC maintenance workers. Because the majority of the accidents could be avoided if some safety precautions were followed, specific safety practices are suggested according to the nature of the operation and the identified leading reasons. A final product of this project is a pre-task safety tool that workers can use before starting any of their typical work operations. This study identifies the leading reasons behind work-related incidents in highway maintenance and the sizable gap in safety measures targeting this unique sector of the construction industry.

Uncertain ground conditions, confined working space, high turnover rate of specialized construction crews increase safety hazards and risks in tunneling. This paper discusses the importance of devising up-to-date and cost-effective adaptive training programs upon enhancing construction safety in tunneling. An adaptive framework is proposed for customizing communication/training tools by site safety personnel while also being capable of keeping feedback from the user end (site trades, project people). The proposed framework comprises three primary sections. Section one presents a training content preparation platform; section two is the safety training content organization; section three presents the systematic and adaptive approach to put the contents into use. A well-organized safety training module is also elaborated, which facilitates knowledge sharing on best work practices in a tunnel under construction or in service. The proposed safety training module architecture emphasizes on increasing awareness, skills building, and action plan. The training program which follows the proposed training architecture ensures that after completing the total training session, the participants can gain comprehensive knowledge in: (1) What and how generic safety-related measures and procedures take place at the work-face space inside tunnel, top of launch shaft and bottom of the pit areas and their relevance to the tunneling operations; and (2) health, safety, occupational hazards, and personal protective equipment requirements in the tunneling environment.

Formwork construction often poses significant threats to workers’ safety because of its temporary nature. Formwork collapses during concrete construction causes injuries, loss of life, property damage, and construction delays. Due to the varied nature of formwork construction, accidents may be a result of several different causes, yet companies need specific areas to focus upon to most effectively improve their safety records. A comprehensive analytical study on these types of accidents would greatly benefit companies that utilize formwork and other temporary structures. Thus, this paper presents statistical analysis results on accident data regarding formwork construction to address whether safety in modern formwork construction is still a problem and what are the main causes to the injuries and fatalities during formwork construction. 435 incidents were obtained from 1984 to 2016 from occupational safety and health administration accident case reports. The data was analyzed to examine causes using ANOVA and Gabriel’s multiple comparisons. Based on the statistical results, the accidents caused by either formwork design or the failure to properly install formwork per design were statistically significant causes for injuries. For fatalities, the statistically significant cause was the failure to properly install formwork as specified in the designs. While these finding contribute to the body of knowledge from the research perspective, companies that utilize formwork or temporary structures could see immense benefits from this analysis, as it could be used to generate the necessary tools and practices to mitigate onsite accidents.

Highway safety continues to be one of the most critical issues facing highway agencies. This paper proposes a hybrid approach for the identification of safety projects via a combination of a conventional regression-based model and spatial analysis techniques. For many transportation agencies, safety projects are identified and prioritized via cost-benefit analyses, multi-attribute ranking criteria, crash frequency modeling, statistical models, and many others. Nonetheless, for many conventional statistical models in the existing literature, the studies only provide statistically significant explanatory variables which influence the frequency and/or injury severity of crashes. More often than not, there is no specific approach, based on the statistical model, which guides highway safety engineers in the identification of high-risk road segments for targeted interventions. Furthermore, there is a deficiency in formalized frameworks which utilizes generalized linear models in tandem with spatial tools to propose specific safety projects based on crash severity. The proposed methodology leverages the generality of findings using regression-based methods with spatial querying and a hot spot analysis (using the Getis-Ord Gi* statistic) method to identify potential locations for safety projects. A synthesized framework is proposed and implemented using crash data for Tarrant County in Texas. This approach allows the integration of expert knowledge from safety engineers and also provides a pragmatic approach to ameliorating crash injury severity on the highway network.

Wildland urban interfaces are exposed to wildfire hazard. Natural and manmade processes have resulted in an increased frequency and severity of wildfires. Expansion in the size of WUIs calls for a sustainable wildfire management plan. Homeowner involvement in mitigation is considered by many researchers a key element of a wildfire resilient community. Given that residential property is the major asset for the majority of homeowners, they are likely to protect the value of their homes through the investment in mitigation activities. Experimental studies, surveys, and interviews with homeowners in wildfire prone WUIs have shown that homeowners see mitigation as a process that takes multiple years to be completed. The investment decision made by homeowners through a 5-year period is investigated in this study. Investment options are private land treatments and adding a wildfire protection coverage to their insurance policy by purchasing extra premium. The objective is to maximize the expected value of the investment throughout the decision period. Three factors that could affect the outcomes are investigated, namely, homeowner’s preference on dynamic trend of investment, time value of money, and the effect of mitigation contingent insurance. A mixed integer programming is used to solve the optimization problem, where the choice of coverage options is represented through integer variables and the investment on retrofit measures are continuous variables. The results show that over a decision period, a homeowner who prefers to invest more on their treatment activities earlier rather than later achieves a higher expected value of investment when compared to a homeowner who prefers to invest more towards the end of the decision period.

According to the Water Research Foundation, over 700 water main breaks occur daily in the United States, leading to huge losses in the distribution of potable water. A 2012 study by Utah State University indicated that ductile iron pipe (DIP) represents a considerable share of the potable water market (28% of pipeline network) in the United States. Among the various diameter classes of DIP, small diameter DIPs (pipes with nominal diameter less than or equal to 12 in.) exhibit the greatest failure rate, owing to thinner pipe walls and larger slenderness ratios. An understanding of the failure modes and mechanisms in small DIPs, and the condition assessment (CA) techniques suitable for detecting flaws and estimating the remaining service life of such pipes, would enable water utilities to identify pipes that are likely to fail, facilitating timely repair and replacement decisions. A qualitative research approach, which included a survey of North American water utilities was selected for this study. This study contributes to the body of knowledge and practice by: (1) identifying the predominant failure modes in small diameter DIPs, and (2) evaluating CA technologies currently used by North American water utilities based on their cost, ease of implementation, and suitability for detecting flaws in small diameter DIPs.

Social risk factors have negative impacts on the implementation of water conservancy projects, and they also affect the stability and security of the regional society. However, how these factors influence each other is lack of study. This paper aims to analyze the inner relationship between the social risk factors of the large-scale water conservancy projects in China. Through a questionnaire survey investigating the cause-effect relationship among 33 social risk factors which are identified from the literature, the data was sought and evaluated. Further, a conducting theoretical model is established. The model shows how risk factors derived from the risk sources of project interior, social individual, and social environment conduct in specific path. The result helps us to understand the conducting path of social risk factors and provides a rational risk conduction model for risk analysis in relevant projects.

In spite of some improvements, more treatments are needed for transportation safety. Advertising signs are among factors that adversely affect drivers’ vision, and increase the number of crashes at some locations. This paper discusses 42 advertising signs located on different freeway sections in Michigan that could contribute to crashes at certain distances from the signs. The total number of crashes was collected from the state crash database for the period from 2009 to 2013. A buffer area of 1,500 ft was defined for before and after each sign using the ArcGIS 10.0 software package. The distribution of the crash distance consisted of two gamma distributions, and parameters of mixed distribution were investigated by employing the expectation maximization algorithm (EM algorithm) as an iterative statistical method. The R software package was utilized for this algorithm to define the parameters of the mixed distribution and to minimize misclassification. Welch’s formula was used to define the border, and Newton’s procedure was employed to find an X value (i.e., identified distance) to satisfy Welch’s formula. Results show the selected advertising signs could increase the number of crashes 552 ft from the signs. A regression model also indicated that among other factors such as land use characteristics, median type, ramp presence, number of lanes, and other type of signs on the freeway sections, these 42 signs have significantly caused an increase in crashes, and it is recommended to relocate them to other parts of freeways in order to improve traffic safety.

Serious games are often considered effective teaching and learning tools due to their ability to engage players through interactive and simulated environments. The authors developed a 3D serious game, SafeDesign, to provide a prevention through design concept training environment in which students assume the role of a construction manager and walk the scenarios to identify potential safety hazards and solutions during pre-construction. In order to develop serious games, game engines demand the knowledge and experience of a game developer who knows how to employ such technology for its particular usage and customize game assets to be created by 3D game artists. These requirements can make game development a costly investment that many educational institutions are not willing to spend in the absence of a convincing evidence of the effectiveness of a particular game. Therefore, the authors critically analyze the state of the art resources for game development and will delineate guidelines for lecturers and trainers to create serious games on their own, without the need for specific programming skills. The paper also offers insights for instructors on how to avoid common pitfalls in the game development process and use low-cost or free resources to rapidly author serious games for education.

The U.S. solar industry has seen a drastic increase in solar systems installed on residential buildings due to the reduced payback periods in recent years. Most solar installations happen on the rooftops of existing houses, creating unique worker safety hazards associated with the existing roof conditions and panel installations. Prevention through design (PtD) has been developed as a proactive method to eliminate safety hazards during the design process. However, no identified study has investigated how PtD can be applied to improve safety in solar panel installations. The objective of this research is to fill this knowledge gap by investigating how, during the process to design the solar panel installations, to address safety concerns in solar system installations for small residential buildings by applying the concept of PtD. Based on a number of interviews and four case studies, this study led to the identification of seven PtD attributes and the development of a safety design protocol that small solar contractors can employ to improve their safety practices. This paper presents the research activities and the PtD attributes that the research team has identified including roof materials, roof slopes, roof accessories, panel layouts, fall protection systems, lifting methods, and electrical systems. The research will provide a valuable contribution to the effort to prevent safety hazards though PtD and improve safety practices in solar installations.