Hosam Olimat

Purpose: This paper aims to develop a performance-based safety modification rate (SMR) framework designed to address selected structural limitations associated with using the experience modification rating (EMR) alone for offsite safety-performance assessment. The framework integrates phase-sensitive leading and lagging indicators across fabrication, transportation and assembly within a multidimensional relative-benchmarking structure. Design/methodology/approach: The study develops a data envelopment analysis (DEA)-based benchmarking framework using 36 expert-prioritized leading and lagging safety indicators identified through a structured literature review and an expert survey of offsite contractors and insurance professionals. A simulated dataset is used to examine model computational workflow, comparative efficiency benchmarking, pattern classification and diagnostic gap assessment. Multicollinearity and dimensionality checks are performed to examine the analytical structure of the illustrative model. Findings: The simulation illustrates how the DEA-based SMR differentiates simulated firm-year profiles based on multidimensional safety performance, establishes a common ideal-referenced safety benchmark, and classifies firms into distinct descriptive performance trajectories. Through its design, the framework represents proactive safety practices and phase-specific operational conditions that are not explicitly captured when EMR is used alone. Research limitations/implications: The model is demonstrated using simulated data due to limited availability of consolidated, multi-year offsite safety datasets. Also, the simulated yearly trajectories are descriptive illustrations and do not constitute statistical longitudinal inference. Future research should validate the SMR using real-world industry data. Practical implications: The SMR provides insurance carriers and offsite contractors with a structured framework for benchmarking multidimensional safety performance, prioritizing safety review, and examining phase-specific performance patterns. It also establishes a structured foundation for integrating proactive safety information into workers’ compensation underwriting and loss-control processes. Originality/value: The study operationalizes an expert-prioritized, leading and lagging set of offsite construction safety indicators within a DEA framework and demonstrates how the resulting scores may support relative benchmarking and performance classification to support insurance decisions.

Off-site construction, a growing alternative to traditional building methods, offers advantages such as reduced environmental impact, improved productivity, and cost efficiency. However, of...

Construction remains one of the most hazardous industries, consistently reporting high rates of workplace injuries and fatalities. Despite advancements in safety regulations and technologies, significant risks persist due to hazardous tasks, including working at heights, operating heavy machinery, and exposure to harmful materials. The establishment of the Occupational Safety and Health Administration in 1971 marked a significant turning point in construction safety, resulting in a decline in workplace fatalities. However, evolving construction methodologies and digital transformations demand continuous research to enhance worker protection and mitigate emerging risks. This study conducts a longitudinal bibliometric analysis to examine the evolution of construction safety research from 1972 to 2025. Using a dataset of 14,174 journal publications from Scopus, the analysis identifies key research trends, technological advancements, and regulatory shifts that have shaped the field. Findings reveal a transition from basic safety regulations to AI-driven hazard detection, digital twins, and IoT-enabled safety monitoring. The study also identifies key contributors, including prominent countries. By tracing both historical and contemporary research trends, this study offers insights into knowledge gaps and provides guidance on future directions. The findings provide valuable insights for researchers, policymakers, and industry professionals, supporting the development of research-informed safety strategies and the integration of emerging technologies in an increasingly complex industry.

With various emerging technologies and integration possibilities, smart facility management has gained wide interest in recent years. Several technologies were introduced to support facilities management and improve decision-making, such as Building Information Modeling (BIM), Internet of Things (IoT), Digital Twin (DT), artificial intelligence (AI), and blockchain. Yet, facility managers still face challenges related to data handling and the actual implementation of these technologies. Thus, this paper explores the trends and integration possibilities of smart facilities management technologies to provide a deeper understanding of the current research state and the areas for future exploration. The Scopus database is utilized to collect literature data, and a bibliometric analysis is conducted on 7236 publications of different types, including conference publications, articles, reviews, and book chapters, using VOSviewer software. The results revealed a noticeable growth in the annual number of publications related to this field after 2018. BIM, IoT, and DT were seen to share the greatest research attention, with BIM being the dominant technology. With recent wide attention, blockchain technology is noticed to be introducing many integration possibilities. In addition, the prominent contributing authors, countries, and sources to this research area are also identified.

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 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.