An Ontology Framework for Addressing Cost Overrun through Risk Modeling: A Risk Path Approach

Whether forced by economic conditions or internal motivations, contractors may choose to minimize their mark-up margins in order to maximize their chances of winning a bid. Such bidding conditions render contractors sensitive towards all types of risks associated with executing a project. This research aims at providing contractors with a framework through which they can reduce their bid prices to be able to compete in low biding conditions. This aim is realized through identifying risk elements that have the greatest impact on projects’ costs in the Egyptian construction industry. Work on this research follows a risk path approach consisting of risk sources, risk events, and risk consequences, and vulnerability factors consisting of robustness factors, resistance factors and sensitivity factors, whose relationships and risk paths are mapped through an ontology model. The weights characterizing that relationship between each of these elements is estimated through a three-phase model that utilizes both optimization and Artificial Neural Networks (ANN), through 52 risks cenarios collected from 35 experts in the Egyptian Construction industry. Outputs generated by the model comprise of five sets of weights. Each set represents the effect of one risk path element on a subsequent element, collectively demonstrating the relations connecting the risk path elements to cost overruns. The model’s outputs showed that that 35 percent of the top 20 Robustness factors are related to project design. Lack of contractor’s technical resources rank higher than that of contractor’s financial resources in terms of their effect on Risk events. Project type has the most impact on project cost overrun, followed by Project delivery method. Further, delays due to bureaucracy whether from the owner or the government’s side rank at the bottom of the list

System Dynamic Modeling To Study The Impact Of Construction Industry Characteristics And Associated Macroeconomic Indicators On Workforce Size And Labor Retention Rate

Limited skilled labor has been one of the greatest challenges facing the construction industry. The COVID-19 pandemic has further exaggerated the already strained construction labor market, leading to an additional negative impact. One of the major contributors to skilled labor shortages in construction is the issue of labor retention. Overall, this is a complex and dynamic situation that requires effective and efficient simulation-based techniques to capture the interdependent relationships that affect the performance of the construction labor market. This paper fills this knowledge gap. To this end, the authors used a multistep research methodology that involved (1) identifying factors that affect skilled labor shortages; (2) developing a one-module system dynamics model that consists of three interconnected systems (namely, construction labor market system, industry characteristics system, and economic conditions system); (3) initializing and calibrating the model to simulate the construction labor market; (4) validating the model through structural, behavioral, and calibration tests; and (5) conducting sensitivity analysis to simulate different parameters and examine their impact on skilled labor shortage. Among other findings, results indicated that all scenarios were successful in improving the conditions of the skilled labor market by increasing the workforce size and labor retention rate. Further, the model demonstrated that economic indicators have a more impactful influence on labor retention patterns compared with industry characteristics. The developed model offers industry practitioners, policymakers, business analysts, and other associated stakeholders a useful tool to test various scenarios including national-level economic policies and labor retention regulations that affect the construction skilled labor market. Consequently, this allows users to analyze the impact of variables such as fiscal policies, economic support plans, and construction spending strategies

Blockchain Technology in the Construction Industry: Mapping Current Research Trends using Social Network Analysis and Clustering

Blockchain represents an evolving technology for distributed and secure recording and sharing of information. Meanwhile, blockchain has thrived in banking, finance, and supply chain; its usage within the construction industry is still in its infancy. To this end, the existing literature falls short in providing comprehensive quantitative understanding, within a systems-based analytic context, of the factors affecting blockchain utilization in construction applications. This paper fills this knowledge gap. The authors: (1) conducted an extensive literature review on blockchain implementation in the construction domain; (2) identified a list of 41 factors affecting blockchain implementation in construction projects categorized in four categories: challenges, needs, requirements, and capabilities; (3) utilized a social network analysis (SNA) approach on a database of 111 publications to quantitatively analyze the literature as related to the aforementioned factors; and (4) performed clustering analysis on the SNA graphs to determine the combinations of factors that are most likely co-occurring in research publications. SNA results indicate that while the most investigated factor was increased trust and transparency between project parties , the least studied factors included: cash upfront funding system , change payment processes and procedures , smart contracts design errors , cryptocurrency fluctuations , lack of sufficiently skilled personnel , and increased awareness and capabilities of personnel . Also, clustering outcomes highlight that some combinations of factors are not well-represented in current scholarly efforts. Such imbalance and consequent knowledge gaps may contribute to the actual implementation rate of blockchain in construction applications. Ultimately, this paper provides a roadmap for potential future directions of blockchain construction-related research

Graduate Recruitment Offers: Ethical and Professional Considerations for Engineering Graduate Students and Faculty Members

This paper investigates the ethical and professional responsibilities of engineering Graduate Students (GSs) and Faculty Members (FMs) in relation to Graduate Recruitment Offers (GROs). The authors developed an academic survey for data collection and subsequently evaluated the collected data based on common ethical theories and principles, as well as relevant professional codes of conduct. Based on the survey responses, this study identified the most common driving and preventive reasons for FMs and GSs not to honor a signed GRO. Further, the perception of GSs and FMs in relation to GROs was investigated using statistical methods. Finally, the authors provided an educational framework in the form of a checklist aimed at promoting ethical and professional decision-making as related to GROs. Ultimately, the outcomes of this research can be incorporated into senior seminar courses to enhance engineering undergraduate students\u27 ethical education and promote their ethical thinking as they grow into professional roles

Journal of Construction Management and Economics 40th Anniversary: Investigating Knowledge Structure and Evolution of Research Trends

Celebrating the Journal of Construction Management and Economics (CME) 40th anniversary, the goal of this paper is to investigate the knowledge structure and evolution of research trends in CME since its inception. The associated objectives include: (1) analyzing CME\u27s scholarly characteristics; (2) studying CME\u27s publication output over time; (3) examining interconnectivities between CME\u27s research trends; and (4) exploring the potential citation impact of recently published CME\u27s papers. In doing so, this paper implemented a multistep methodology that consists of descriptive assessment, social network analysis (SNA), and predictive machine learning (ML). Results of descriptive assessment showed that CME has witnessed over the years a noticeable growth in the number of publications, citation trends, and collaborative research as depicted increased co-authorship, and that highest percentage of publications were related to Strategy, Decision Making, Risk, and Finance , Project planning and Design and Contemporary Issues . Output of SNA highlights that research areas with the highest interconnectivity included Strategy, Decision Making, Risk and Finance and Project Planning and Design , and Labor and Personnel Issues . Furthermore, predictive ML revealed that CME papers have a high probability of becoming high impact publications. In addition to that, the predictive ML results re-emphasized the outcomes of the performed descriptive assessment by reflecting the importance of Contemporary Issues , Organizational Issues , Strategy, Decision Making, Risk, and Finance , and Labor and Personnel Issues as emerging research topics with increased potential impact in the future. Ultimately, this paper benefits all CME stakeholders by quantitatively studying current research patterns, their interconnectivities, and future potential