There is a growing consensus that transportation construction projects have an influence beyond the provision of reliable infrastructure and significantly impact socioeconomic advancements. This is reflected in recent global infrastructure development initiatives, which encompass diverse objectives related not only to technical aspects but also nontechnical ones. However, it is challenging to synthesize diverse and sometimes competing objectives in program development. Well-established frameworks or formulas supporting the synthesis and delivery of objectives do not always exist. Ex post empirical analysis of program development outcomes, rather than planning documents, is scarce. In this study, the authors applied an explainable machine learning method, specifically, a zero-inflated negative binomial model with two classification parts, supported by a genetic algorithm, the extreme gradient boosting algorithm, and SHapley Addictive explanation values, on the project inventory supported by discretionary transportation programs of Canadian federal governments over an eight-year period. The project records were mapped to the three categories of community attributes corresponding to the three objectives of the Canadian infrastructure initiative: resilient infrastructure; economic growth; and equity promotion. The results reveal a dichotomous reality: Although transportation equity could be a significant factor in rural areas, the communities where most Canadians live host major transportation construction projects because of factors related to transportation assets and local economics. Bridge improvement is a focal theme, and a vibrant local economy is generally favored. Percentages of senior citizens and children might adjust the level of development. It is anticipated that the findings will provide policy decision-makers, transportation asset owners, and community leaders with a realistic understanding of the realities of discretionary transportation program development, thereby informing evidence-based decision-making in the future.

Despite advancements in safety measures, fatalities in the transportation sector remain disproportionately higher than in other industries. Transportation sector fatalities arise from a combination of demographic, environmental, and occupational factors that amplify the overall risk. While strides have been made in understanding the factors contributing to fatalities, there remains a critical gap in research focused on their temporal evolution. The goal of this study is to investigate the temporal evolution of fatality-linked association factors in the transportation construction sector. The methodology encompassed three stages. Stage 1 was preprocessing the data: the US Census of Fatal Occupational Injuries, covering 3,792 fatality cases with 12 different features and 630 associated categories. Stage 2 applied the Apriori algorithm to generate if–then rules and identify critical factors. Stage 3 conducted a change mining analysis to examine temporal shifts in the rules for three demographic groups over three time periods, highlighting the risky combinations of features in the fatality data. Analysis revealed that for the first demographic group (White males), a critical combination of factors included (1) being an unskilled construction laborer; (2) working in the private sector; and (3) being struck by mobile equipment. But from a temporal lens, the significance of this combination decreased over time, potentially reflecting an industry-wide intervention. For the second group (males of other races), risky combinations evolved from pickup truck incidents involving employees (1) aged 20–24 years; and (2) with less than 1 year tenure in private ownership to work zone incidents involving (1) a forward-moving vehicle; (2) employees of large firms; and (3) projects in the South region. Finally, the third group (females) shows a consistent combination: (1) directing traffic; and (2) being a crossing guard. This work sheds light on historical trends and serves as a foundation for informed decision-making and targeted intervention strategies.

Climate change has led to increasing frequency and intensity of extreme weather events worldwide, making cities more vulnerable to urban flooding. While the direct impacts of flooding on safety and property damage have been extensively studied, the indirect effects of disrupted accessibility to essential facilities and services remain a critical yet under-addressed challenge. Current work often overlooks the complexity of geographical dependencies and the diverse nature of accessibility needs, leading to suboptimal flood mitigation efforts. This study proposes an integrated accessibility-driven framework for identifying critical flood hotspots and prioritizing flood mitigation interventions. The framework consists of three key components: (1) quantifying the importance of different facilities by considering residents’ diverse needs for urban services during flood conditions; (2) developing an integrated accessibility index that incorporates multiple facility types weighted by their importance; and (3) proposing a greedy restoration strategy that prioritizes the repair of flood hotspots based on their impact on the integrated accessibility. The proposed framework is demonstrated through a case study in the core urban area of Beijing. The results reveal that repairing flood hotspots based on their impact on integrated accessibility leads to a more rapid and effective recovery of regional accessibility compared to other strategies. A comparative analysis of different flood management strategies highlights the effectiveness of the proposed accessibility-based approach in guiding flood mitigation efforts. This research provides a quantitative tool for urban planners and decision makers to enhance the equity and resilience of urban systems in the face of increasing flood risks.

Environmental, social, and governance (ESG) principles create value for the organizations that practice them and for the people they serve. Most ESG efforts—and literature—have centered on the finance industry. Whereas the architecture, engineering, and construction (AEC) industry has increased its focus on ESG over the past couple of decades, the literature is sparse in its treatment of the topic, especially as it relates to AEC workforce development. Further, most literature on academic–industry partnerships focus on research, rather than education. To advance ESG in the AEC industry, academic programs must prepare its graduates with relevant knowledge, skills, and abilities; accreditation is one of the most prominent drivers that shapes academic programs, and hence, the capabilities of graduates. Using policies and practices from the US Army Corps of Engineers and accreditation requirements from the Engineering Accreditation Commission of ABET, this illustrative case study illuminates some of the opportunities related to advancing ESG in the AEC industry through collaborative engineering education and practice. Thus, this research addresses gaps in the body of knowledge relative to both AEC workforce development and academic–industry partnerships centered in ESG principles and practices.

Effective safety management at construction sites requires accurate hazard recognition. However, previous research studies have revealed a significant proportion of hazards remain unrecognized. Poor hazard recognition may be attributed to the deficiency at one or more levels of situational awareness (SA). Understanding how different hazard types, particularly those with high fatality rates, influence workers’ SA is critical to improving safety outcomes. To address this research gap, three hazard types with different fatality hierarchies (F-I: fall, F-II: struck-by, and F-III: electrical hazards) were built in a complex and dynamic virtual reality (VR) construction environment. Participants’ hazard recognition behaviors regarding hazard perception, comprehension, and projection (Level 1, 2, and 3 SA) were measured by a well-recognized situational awareness global assessment technique (SAGAT). The findings revealed that the achievement of SA varied among different hazard types and was impacted by fatality hierarchy. There was a consistent decline in success rates from Level 1 to Level 3 SA across all hazards, and the potential to achieve a high-level SA demonstrated a positive correlation with the hazard fatality hierarchy. Specific reasons for failure at each SA level attributed to shortcomings included a deficiency in prior knowledge, narrowed attention, and the increased demand for cognitive resources. The findings enhance the understanding of workers’ SA in hazard recognition behaviors and provide a foundation for developing customized interventions. These interventions can be tailored to address SA deficiencies at different levels based on hazard types with distinct fatality hierarchies, ultimately improving safety performance at construction sites.

Contracting parties in the architectural, engineering, and construction (AEC) industry often miss out on the benefits of constructive contractual conflicts and instead face adverse outcomes due to destructive ones. Studies suggest that embracing artificial intelligence (AI) can improve the management of destructive contractual conflicts. However, a gap remains in capturing a holistic view of applying an AI–driven contractual conflict management (CCM) system to manage these conflicts before they escalate into claims and disputes. Thus, this study aims to fill this research gap by answering three research questions: RQ1: What are the perceived benefits of using AI in managing contractual conflicts in the AEC industry? RQ2: What is the current state of the AEC industry regarding managing contractual conflicts and implementing an AI–driven CCM system to address these conflicts? RQ3: What core elements and strategies are needed to implement an AI–driven CCM system in the AEC industry? Data collected from 32 experts through semistructured interviews revealed that AI could aid in data analysis, conflict prediction, operational efficiency, decision analysis, negotiation, and collaboration processes within the industry. Despite facing several challenges in managing contractual conflicts, the industry is at a critical stage in implementing an AI–driven CCM system. This study identified and discussed the importance of implementing a legally framed, trustful, prevention-oriented, and human-centered AI–driven system. It also discussed the associated ethical issues and proposed frameworks to ensure a lawful, trustworthy, and human-centered AI–driven CCM system. Furthermore, it proposed an iterative implementation strategy, including formulation, context adaptation, piloting, optimization, and large-scale implementation. This study outlines theoretical and practical contributions and suggests potential research areas to address the study’s limitations.

Environmental information disclosure (EID) as an essential component of corporate social responsibility (CSR) has become a business imperative worldwide. Despite the growing interest in CSR activities of construction companies on an international scale, little is known about the extent to which firms’ EID practices are influenced by their internationalization. This study thus fills the gap using a sample of 1,571 observations from 250 public construction companies headquartered in 36 countries. The level of internationalization is measured as the ratio of foreign sales to total sales. The empirical results suggest that the level of internationalization has a significant and positive impact on the quality of EID—the extent to which a firm’s disclosed information represents its overall environmental performance. The findings remain robust after conducting a batch of robustness checks and addressing potential endogenous concerns. Additionally, the results demonstrate that the positive effect of internationalization on the quality of EID is more pronounced for firms that are (1) more exposed to adverse CSR events, (2) headquartered in countries with less stringent environmental policies, and (3) headquartered in countries with fewer ties to global information trends. Collectively, this study sheds light on the implications of internationalization for environmental disclosure and transparency.