Determining Safety Inspection Thresholds for Employee Incentives Programs on Construction Sites

An evolutionary game-theoretic analysis of construction workers' unsafe behavior: Considering incentive and risk loss

The behavior of construction workers has a significant impact on the overall safety climate of a project. The purpose of this paper is to figure out the evolutionary pattern of workers' unsafe behavior and to minimize its occurrence. We constructed a two-sided evolutionary game model consisting of workers and managers to explore the focal point of interest, strategy equilibrium conditions, and behavior evolution process. The experimental results of stability analysis and system dynamics show that there are two stable states in all four cases, (Safe behavior, Negative management) as well as (Unsafe behavior, Negative management). The lower the initial willingness of workers to behave unsafely, the faster they reach a safe steady state. By contrast, managers' strategy choices have a certain lag. Workers are discouraged from choosing unsafe behavior under both the positive incentive of raising bonuses and the negative incentive of raising fines. And the sensitivity of the two incentives is similar. For indirect effect risk loss, when it is effectively controlled during safe construction, workers quickly gravitate toward safe behavior. These findings provide a reference for construction safety management. Several practical suggestions were proposed from three perspectives: the worker, the manager, and the site safety climate, focus on the theme of reducing unsafe behavior and achieving a virtuous cycle of safety climate.

Evolutionary Game Analysis of Construction Workers' Unsafe Behaviors Based on Incentive and Punishment Mechanisms

Construction is one of the most dangerous industries because of its open working environment and risky construction conditions. In the process of construction, risk events cause great losses for owners and workers. Most of the risk events are closely related to unsafe behaviors of workers. Therefore, it is of great significance for contractors to establish management measures, e.g., incentive and punishment mechanism, to induce workers to reduce unsafe behaviors. This paper aims to take the incentive and punishment mechanism into consideration and develop an evolutionary game model to improve the effectiveness of safety management. The evolutionary stability strategies which can help reduce unsafe behaviors are obtained and analyzed. Results show that there are 12 equilibrium strategies under the condition of different parameters. Specifically, the incentive and punishment mechanism has played an important role for the evolution direction. A balanced incentive and punishment mechanism for the investment and positive stimulus for workers can effectively promote both sides to take positive behaviors, and then realize good evolutionary stable situations. In addition, the initial perceptions of both sides have a decisive impact on the evolution direction. Strengthening communication with the mutual trust between both sides can improve safety performance of both sides. This study is valuable for contractors to design appropriate incentive and punishment measures and establish relevant strategies to promote safe behaviors of construction workers.

Organizational factors and specific risks on construction sites

INTRODUCTION

This study develops an empirical test of two theoretical models using the approach of Structural Equation Model (SEM) to test the relationships between specific organizational factors of safety management system (SMS) and specific risk variables.

METHOD

Two SEM models with two and four latent variables, respectively, and 10 observed risk variables were used to identify the strongest relationships that may lead to an accident on site. A random sample of 474 construction sites were visited and assessed in Spain from 2003 to 2010. Most of the samples were small and medium sized enterprises (SMEs), which is the predominant type of company in the Spanish construction industry. To assess the risk on sites and get the measurements of the variables included in the models, the validated method CONSRAT (Construction Sites Risk Assessment Tool) was used. After estimating the proposed models, an adequate fit was obtained for both of them.

RESULTS

Results provide empirical evidence that: (a) the factor "Resources on site" is more determinant in explaining influences on risk variables because of their influence on all risk variables (Model 1); (b) the factor "Site structure complexity" (which includes structure and organization, and safety resources available on site) has a stronger effect on risk variables than other factors related to intrinsic characteristics of the work, site, or companies (Model 2).

CONCLUSIONS

These results mean that the complexity and resource factors that depend on companies are those that have the greatest impact on risks, which makes it possible for companies to undertake the appropriate risk control measures.

PRACTICAL APPLICATION

These results can help construction firms obtain earlier information about which organizational elements can affect future safety conditions on site, improve those elements for preventing risks, and consequently, avoid accidents before they occur.

Characterizing accident narratives with word embeddings: Improving accuracy, richness, and generalizability

INTRODUCTION

Ensuring occupational health and safety is an enormous concern for organizations, as accidents not only harm workers but also result in financial losses. Analysis of accident data has the potential to reveal insights that may improve capabilities to mitigate future accidents. However, because accident data are often transcribed textually, analyzing these narratives proves difficult. This study contributes to a recent stream of literature utilizing machine learning to automatically label accident narratives, converting them into more easily analyzable fields.

METHOD

First, a large dataset of accident narratives in which workers were injured is collected from the U.S. Occupational Safety and Health Administration (OSHA). Word embeddings-based text mining is implemented; compared to past works, this methodology offers excellent performance. Second, to improve the richness of analyses, each record is assessed across five dimensions. The machine learning models provide classifications of body part(s) injured, the source of the injury, the type of event causing the injury, whether a hospitalization occurred, and whether an amputation occurred. Finally, demonstrating generalizability, the trained models are deployed to analyze two additional datasets of accident narratives in the construction industry and the mining and metals industry (transfer learning). Practical Applications: These contributions improve organizations' capacities to rapidly analyze textual accident narratives.

Factors Influencing Safety on Construction Projects (fSCPs): Types and Categories

Due to the fact of activity, environment and work dynamics, the construction industry is characterised by high accident rates. Different initiatives have emerged to reduce these figures, which focus on using new methodologies and technologies for safety management. Therefore, it is essential to know the key factors and their influence on safety in construction projects (fSCPs) to focus efforts on these elements. Through a systematic literature review, based on PRISMA methodology, this article identifies, describes and categorises 100 factors that affect construction safety. It thus contributes by providing a comprehensive general framework, unifying previous studies focused on specific geographic areas or case studies with factors not considered or insufficiently disaggregated, along with an absence of classifications focused on understanding where and how factors affect the different dimensions of construction projects. The 100 factors identified are described and categorised according to the dimensions and aspects of the project in which these have an impact, along with identifying whether they are shaping or immediate factors or originating influences for the generation of accidents. These factors, their description and classification are a key contribution to improving the systematic creation of safety and generating training and awareness materials to fully develop a safety culture in organisations.

Testing the associations between leading and lagging indicators in a contractor safety pre-qualification database

BACKGROUND

Safety prequalification assessing contractors' safety management systems and safety programs lack validation in predicting construction worker injuries.

METHODS

Safety assessments of leading indicators from 2198 construction contractors, including Safety Management Systems (SMS), Safety Programs (e.g., falls, hearing protection), and Special Elements (drug testing, return to work) scales as well as the history of citations from the Occupational Safety and Health Administration (OSHA) were compared to contractors' lagging indicators of recordable injury case rates (RC) and rates of injuries involving days away, restricted, or transferred (DART).

RESULTS

Increased SMS scores were related to lower injury rates. Each one-point increase in SMS values was associated with 34% reduced odds of a recordable case rate greater than zero (Odds ratio (OR): 0.66, 95% Confidence Interval (CI): (0.57, 0.79)), and a 9% reduced recordable case rate, if one occurs (Risk Ratio (RR): 0.91, 95% CI: (0.88, 0.94)). A one-point increase in SMS was associated with 28% reduced odds of a DART (OR = 0.72, 95%CI (0.56, 0.91)), and 9% reduced DART rate, if one occurs (RR = 0.91, 95%CI (0.87, 0.95)). Safety programs did not show consistent associations with injury outcomes. Having additional Special Elements related to drug and alcohol programs was associated with lower injury rates while the Special Element related to return to work showed no consistent associations with injury. Having more OSHA Citations was associated with lower injury rates for companies with injuries.

CONCLUSIONS

These results support pre-qualification methods based on SMS and suggest the need for safety management systems in contractors.

Effects of organizational complexity and resources on construction site risk

INTRODUCTION

Our research is aimed at studying the relationship between risk level and organizational complexity and resources on constructions sites. Our general hypothesis is that site complexity increases risk, whereas more resources of the structure decrease risk. A Structural Equation Model (SEM) approach was adopted to validate our theoretical model.

METHOD

To develop our study, 957 building sites in Spain were visited and assessed in 2003-2009. All needed data were obtained using a specific tool developed by the authors to assess site risk, structure and resources (Construction Sites Risk Assessment Tool, or CONSRAT). This tool operationalizes the variables to fit our model, specifically, via a site risk index (SRI) and 10 organizational variables. Our random sample is composed largely of small building sites with general high levels of risk, moderate complexity, and low resources on site.

CONCLUSIONS

The model obtained adequate fit, and results showed empirical evidence that the factors of complexity and resources can be considered predictors of site risk level.

PRACTICAL APPLICATIONS

Consequently, these results can help companies, managers of construction and regulators to identify which organizational aspects should be improved to prevent risks on sites and consequently accidents.

Occupational safety and health in construction: a review of applications and trends

Due to the high number of accidents that occur in construction and the consequences this has for workers, organizations, society and countries, occupational safety and health (OSH) has become a very important issue for stakeholders to take care of the human resource. For this reason, and in order to know how OSH research in the construction sector has evolved over time, this article-in which articles published in English were studied-presents an analysis of research conducted from 1930 to 2016. The classification of documents was carried out following the Occupational Safety and Health Cycle which is composed of five steps: regulation, education and training, risk assessment, risk prevention, and accident analysis. With the help of tree diagrams we show that evolution takes place. In addition, risk assessment, risk prevention, and accident analysis were the research topics with the highest number of papers. The main objective of the study was to contribute to knowledge of the subject, showing trends through an exploratory study that may serve as a starting point for further research.

Understanding the relationship between safety investment and safety performance of construction projects through agent-based modeling

The construction industry in Hong Kong increased its safety investment by 300% in the past two decades; however, its accident rate has plateaued to around 50% for one decade. Against this backdrop, researchers have found inconclusive results on the causal relationship between safety investment and safety performance. Using agent-based modeling, this study takes an unconventional bottom-up approach to study safety performance on a construction site as an outcome of a complex system defined by interactions among a worksite, individual construction workers, and different safety investments. Instead of focusing on finding the absolute relationship between safety investment and safety performance, this study contributes to providing a practical framework to investigate how different safety investments interacting with different parameters such as human and environmental factors could affect safety performance. As a result, we could identify cost-effective safety investments under different construction scenarios for delivering optimal safety performance.

Improving safety climate through a communication and recognition program for construction: a mixed methods study

OBJECTIVES

This study aimed to evaluate the efficacy of a safety communication and recognition program (B-SAFE), designed to encourage improvement of physical working conditions and hazard reduction in construction.

METHODS

A matched pair cluster randomized controlled trial was conducted on eight worksites (four received the B-SAFE intervention, four served as control sites) for approximately five months per site. Pre- and post-exposure worker surveys were collected at all sites (N=615, pre-exposure response rate of 74%, post-exposure response rate of 88%). Multi-level mixed effect regression models evaluated the effect of B-SAFE on safety climate as assessed from surveys. Focus groups (N=6-8 workers/site) were conducted following data collection. Transcripts were coded and analyzed for thematic content using Atlas.ti (version 6).

RESULTS

The mean safety climate score at intervention sites, as measured on a 0-50 point scale, increased 0.5 points (1%) between pre- and post-B-SAFE exposure, compared to control sites that decreased 0.8 points (1.6%). The intervention effect size was 1.64 (3.28%) (P-value=0.01) when adjusted for month the worker started on-site, total length of time on-site, as well as individual characteristics (trade, title, age, and race/ethnicity). At intervention sites, workers noted increased levels of safety awareness, communication, and teamwork compared to control sites.

CONCLUSIONS

B-SAFE led to many positive changes, including an improvement in safety climate, awareness, teambuilding, and communication. B-SAFE was a simple intervention that engaged workers through effective communication infrastructures and had a significant, positive effect on worksite safety.

Development of a safety communication and recognition program for construction

Leading-indicator-based (e.g., hazard recognition) incentive programs provide an alternative to controversial lagging-indicator-based (e.g., injury rates) programs. We designed a leading-indicator-based safety communication and recognition program that incentivized safe working conditions. The program was piloted for two months on a commercial construction worksite and then redesigned using qualitative interview and focus group data from management and workers. We then ran the redesigned program for six months on the same worksite. Foremen received detailed weekly feedback from safety inspections, and posters displayed worksite and subcontractor safety scores. In the final program design, the whole site, not individual subcontractors, was the unit of analysis and recognition. This received high levels of acceptance from workers, who noted increased levels of site unity and team-building. This pilot program showed that construction workers value solidarity with others on site, demonstrating the importance of health and safety programs that engage all workers through a reliable and consistent communication infrastructure.

Predicting subcontractor performance using web-based Evolutionary Fuzzy Neural Networks

Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.