The construction work environment remains one of the most hazardous among all industries. Construction injuries directly impact the workers and the work itself, including personal suffering, construction delays, productivity losses, higher insurance premiums, and possible liability suits for all parties involved in the project. The costs resulting from personal injuries, combined with the associated financial impact resulting from schedule disruptions, insurance hikes, and workers’ compensation, can impact a project’s profitability. Many of these impacts can be minimized or avoided through the continuous assessment and improvement of safety policies and practices. This paper aims to propose a new safety assessment methodology that equips insurance companies and construction managers with an optimal mechanism for evaluating the safety performance of construction companies. The proposed model consists of 20 evaluation criteria that are used to establish the efficiency benchmarks and provide comparison feedback for improving the company’s safety plans and procedures. These criteria are determined based on leading and lagging safety performance indicators. The data envelopment analysis (DEA) technique is used as the underlying model to assess the relative efficiency of safety practices objectively. Two illustration case studies are provided to demonstrate the dual effectiveness of the DEA model. The presented research contributes to the body of knowledge by formalizing a robust, effective, and consistent safety performance assessment. The model equips the company with the ability to track both the progression and the retrogression over time and provides feedback on ineffective practices that need more attention. Simultaneously, the model gives them more detailed safety performance information that can replace the current experience modification rating (EMR) approach. It provides insurance companies with an objective and robust evaluation model for selecting optimum rates for their clients. In addition, the data comparison utility offered by the DEA model and its criteria can be helpful for insurance companies to provide effective advice to their clients on which safety aspects to improve in their future strategies.

Construction is one of the most hazardous industries worldwide. Implementing safety regulations is the responsibility of all parties involved in a construction project and must be performed systematically and synergistically to maximize safety performance and reduce accidents. This study aims to examine the level of safety compliance of construction personnel (i.e., top management, frontline supervisors, safety coordinators/managers, and workers) to gain insight into the top safety measures that lead to no major or frequent accidents and to predict the likelihood of having a construction site free of major or frequent accidents. To achieve the objectives, five safety measures subsets were collected and modeled using six combinations of five different Bayesian networks (BNs). The performance of these model classifiers was compared in terms of accuracy, sensitivity, specificity, recall, precision, F-measure, and area under the receiver operating characteristic curve. Then, the best model for each data subset was adopted. The inference was then performed to identify the probability of the commitment to safety measures to reduce major or frequent accidents and recommend enhancement regulations and practices. While the context in this paper is the Jordanian construction industry, the novelty of the work lies in the BN modeling methodology and recommendations that any country can adopt for evaluating the safety performance of its construction industry. This research endeavor is, therefore, a significant step toward providing knowledge about the top safety measures associated with reducing accidents and establishing efficiency comparison benchmarks for improving safety performance.

The design-build project delivery method allows owners to deal with a single source of responsibility for the design and construction phases of a facility. Due to its inherent complexity, there is a serious need for an optimal mechanism that can identify and select qualified organizations to successfully deliver projects under this technique. Therefore, the goal of the research project presented in this paper is to develop a multi-criteria decision-making model and framework that will assist owners in selecting qualified design-build organizations. The Data Envelopment Analysis (DEA) technique is utilized for developing an efficient multi-criteria selection model and framework that are based on the identified quantitative and qualitative criteria. The DEA-based multi-criteria decision-making framework provides a hybrid methodology that integrates the DEA linear programming approach with the importance weights of each of the identified criteria. As such, the DEA-based multi criteria selection framework proposed in this research endeavor addresses the identified limitations of existing models and provides an effective, robust, and consistent selection model coupled with a flexible framework that allows for screening bidders based on both the preferred values as set by the owner and the full DEA core model criteria.

The specialty electrical and mechanical contracting sectors provide crucial services and perform functions that are vital to the products delivered by the construction industry. The main purpose of this study is to investigate the causes of fatal and nonfatal injuries in these specialty construction sectors over time as well as their effects on the level of safety performance in the industry. Accordingly, the most prevalent causes of fatal and nonfatal incidents in the mechanical and electrical sectors are investigated and presented as a longitudinal study from 2005 to 2015. The trends in occupational injuries in these sectors over this period of time are also compared with the trends reported in previous studies. The results from this study show that the direct causes of fatal and nonfatal injuries in the electrical and mechanical sectors differ from those found in the construction industry in general. In addition, the electrical and mechanical construction industry trends identified in this study are similar to previously reported trends. The similarities between the current findings and those of previous studies highlight real shortcomings in the safety management approaches within the construction industry. Based on the findings of this study, a learning investigation system has been recommended to improve safety performance among electrical and mechanical specialty contractors.

Public transportation services often face challenges in middle- and low-income nations from both public acceptance and economic constraints. Jordan is classified as a middle-income country with a population of over 10 million, 4 million of whom live in Amman, the capital. The development and operation of a bus rapid transit (BRT) system in Amman city was recently proposed. The BRT project is anticipated to offer a solution to the city’s escalating congestion problem. This study’s objective is to conduct a “before” analysis to identify the variables that affect the willingness of people to use the Amman BRT system. The socioeconomic characteristics and travel habits of individuals were used to model the willingness to use BRT. An online survey was distributed to Amman residents and 238 valid responses were returned. Two popular techniques were utilized: binary logistic regression and Bayesian networks. Ten models were developed: one binary logistic model and nine Bayesian network models. The results of these models were compared based on accuracy, sensitivity, specificity, area under the Receiver Operating Characteristic (ROC) curve, complexity, and number of selected variables. It was found that Bayesian networks were more effective in modeling willingness to use BRT. Willingness to use BRT was shown to be higher among households without cars, youths, females, and university students, and if there were fewer transfers along the route. It became clear that introducing a new public transportation system is well appreciated, particularly in areas with low income, insufficient existing public transportation services, and where driving a car is the norm.