High-profile failures of infrastructure systems are often attributed to design flaws. However, these incidents can also stem from a broader set of interconnected decisions made by stakeholders involved in the planning, management, and operation of these systems. While ethical guidelines and professional codes are designed to prevent such failures, cognitive biases may undermine adherence. Previous research efforts, primarily focused on ethical violations using case studies, have overlooked how behavioral ethics constructs and professional conduct could drive these breaches. This paper addresses this knowledge gap by (1) conducting an analysis of three system failure cases at the micro, meso, and macro levels to identify key decisions made by primary stakeholders; (2) overlaying these decisions with violations of the ASCE Code of Ethics as well as the known behavioral constructs that triggered ethical breaches; (3) providing quantitative metrics to assess the impact and extent of these decisions on project failure; and (4) constructing visual graph networks to reveal critical patterns of behavioral constructs and code violations. Results showed that conformity, slippery slope, and overconfidence biases negatively influenced decision-making, while the moving spotlight effect had a positive influence. These behavioral constructs formed a self-reinforcing cycle of unethical decisions that explicitly emphasized the need for targeted interventions. As such, ethical risk factors were identified as early warning signs to proactively mitigate unethical decision-making. Moreover, several recommendations were proposed including educating professionals and students about the impact of behavioral biases and implementing organizational reforms that promote transparency, accountability, and diverse perspectives. Through addressing these behavioral patterns, this study offers actionable strategies for professionals, policymakers, and educators to mitigate unethical decision-making and hopefully prevent future infrastructure system failures.

Construction material markets are often reliant on imported commodities. Import tariffs and temporary trade barriers disrupt supply chains, presenting stakeholders with uncertainties regarding how such disruption impacts construction material prices. Despite the plethora of research efforts that examine the effects of economic conditions on construction material prices, the impacts of the import quantities remain understudied. This paper fills this knowledge gap usig a multistep methodology. First, the prices of major construction materials and the quantities of the relevant imported commodities are retrieved. Second, the stability of their historical trends was assessed using econometrics-based tests. Third, a nonlinear autoregressive distributed lag (NARDL) framework is employed to investigate the potentially asymmetric impacts of increases and decreases in import quantities on the construction material prices. The framework is demonstrated using the prices of 15 construction materials in the United States and import quantities from Canada, Mexico, China, and world total. For the 15 materials, prices respond asymmetrically to import shocks; increases and decreases in import quantities have unequal effects for at least one source country. Mexican hot-rolled steel surges are followed by a 2.5% rise in US prices in the long term, while a 10% increase in Canadian fabricated metal imports is accompanied by a short-term price decline of 0.66% at a five-month lag. The results indicate that local production capacity of cement and concrete and crude oil prices drive cement-dependent material prices more than import flows. Due to construction supply chain effects, the prices of prefabricated structural wood members increase by 18.1% in the long run when global lumber imports decline. By modeling how import shocks propagate through upstream and downstream prices, the NARDL framework facilitates prescient procurement strategies of construction materials amid persistent supply chain volatility and evolving trade policies. Owners, contractors, and other associated stakeholders can simulate a shift in steel, cement, or lumber inflows months ahead, identify vulnerable supply lines, and adjust budgets, bids, and contracts proactively.

Global energy is transforming, with nuclear power emerging as a pivotal player in achieving sustainable and low-carbon energy goals. The nature of nuclear technology introduces unique challenges, such as stringent safety and environmental requirements. Existing studies focus on general risk identification, such as supply chain delays, cost overruns, and public perception issues. However, these studies fail to address integrating these risks into tailored contractual provisions, which are critical for navigating the unique challenges of nuclear projects. The goal of this paper is to examine how standard construction contracts can be tailored to better accommodate the risks inherent in nuclear power plant construction. This study employs a multistep methodology. It begins with a comprehensive literature review using the Scopus search engine to identify construction management risks and associated keywords specific to nuclear projects. These risks are analyzed using latent Dirichlet allocation (LDA) to objectively identify relevant clauses within a standard construction contract. By leveraging associated keywords, LDA determines whether the identified risks are adequately addressed in the contractual clauses. Cosine similarity is then used to measure the alignment between the risks and the extracted clauses. Finally, the study integrates industry experience by reviewing nuclear-specific guidelines, reports, and lessons learned to address risks not sufficiently covered in standard contracts and then validating the integration through industry experts. Results showed that the most neglected risk under the standard contract was stakeholder relations and cultural differences. It was recommended to include stakeholder mapping exercises, appoint dedicated cultural mediators, and establish multilingual communication protocols to bridge language barriers and mitigate cultural misunderstandings in nuclear projects. By systematically analyzing risks and aligning them with contractual provisions, this research provides a framework for improving risk management in high-stakes nuclear construction. The findings highlight the necessity of adapting standard contracts to address nuclear-specific challenges.

Skilled labor shortages are a pressing issue in the construction industry. Existing research has primarily focused on the effects of labor shortages at the project or industry level, but there is limited exploration of how these shortages vary across specific trades and their distinct impacts on project outcomes. Evidence indicates that labor shortages vary significantly among trades. This paper addresses this knowledge gap through quantitatively assessing the criticality of key construction trades based on the (1) extent of skilled labor shortages currently witnessed in each trade, (2) impact of these shortages on cost and schedule performance, and (3) degree of industry reliance on each trade. To this end, the cost and schedule criticalities of 10 key construction trades were determined based on a risk assessment using data collected from 106 industry experts. Further, the correlation between labor shortages and project performance was assessed using Pearson's correlation coefficient test. Last, trade criticalities were assessed in relation to the level of reliance of the construction industry on each of the examined trades using Monte Carlo simulation. Results indicate that skilled labor shortages vary across trades, with mechanical, electrical, and plumbing (MEP) trades (electrical and plumbing) facing the most severe shortages, while finishes trades experience relatively lower impacts. Despite moderate shortages, concreting and ironworking significantly affect project cost and schedule performance. Schedule criticality generally surpasses cost criticality, except in electrical and plumbing trades. Furthermore, concreting, ironworking, electrical, plumbing, and masonry were identified as the most critical trades, posing the highest risks to project outcomes. Ultimately, this study provides project stakeholders an integrated assessment of the criticality of the skilled labor shortage in relation to project cost and schedule performance. The latter allows for prioritizing critical trades and as such developing trade-specific strategies to address shortages and mitigate their effects on project outcomes.

Abstract Departments of transportation commonly adopt trigger-based price adjustment clauses (PACs) to share risks of volatile material prices. Although existing research found no statistical evidence that the inclusion of PACs lowers contractors’ bids, qualitative studies have revealed that stakeholders continue to perceive PACs as beneficial. This leaves PACs’ ability to achieve a true risk-sharing equilibrium unresolved. As such, there is a dire need for a systematic investigation of PACs incorporating market volatility, project durations, and contractor bidding and behavioral strategies. This paper evaluates the effectiveness of PACs in facilitating equitable risk-sharing under varying market scenarios and project timelines. A comprehensive three-step approach is adopted through (1) retrieving and analyzing historical price data to capture prevailing trends; (2) using a Bayesian Nash equilibrium under game theory across 324 project scenarios to derive optimal strategic insights; and (3) applying decision tree classification to generalize the findings. Findings indicated that when prices experience a significant inflation throughout the project’s lifetime, a DOT’s best strategy is to apply high trigger values. However, there is a lack of equilibrium under such high trigger values because the results show that the contractors’ best strategy is to overbid. The same equilibrium status for contractors (i.e., overbidding) manifests in balanced market conditions. To this end, DOTs can better manage this by setting average trigger values and implementing performance-based incentives. When prices decline, scenarios suggest that retaining PACs while eliminating trigger values has proven most effective toward equilibrium. This study challenges the assumption that PACs foster equitable risk-sharing, advocating instead for data-driven, dynamic approaches that reflect real-time market behavior rather than static thresholds.

Green and sustainable building practices have emerged as effective strategies to address the environmental challenges posed by the construction industry. This includes unsustainable resource utilization, excessive waste production, greenhouse gas emissions, and high energy consumption. However, the integration of green and sustainable initiatives in construction introduces distinct risks and challenges that affect project management and may lead to claims and disputes. To this end, and while previous research examined dispute causation across various aspects of the construction industry, there is limited investigation of the unique and complex nature of risks and challenges-and consequently conflicts, claims, and disputes-arising specifically from the integration of green and sustainable practices or objectives into construction projects. This paper addresses this knowledge gap through a multistep research methodology. First, 46 litigation cases involving disputes related to green and sustainable construction activities and projects in the US were collected and analyzed. Second, network and clustering analyses were employed to visualize and categorize the groups of factors contributing to disputes in green construction. Third, association rule analysis (ARA) was conducted to identify critical co-occurrences of factors that lead to disputes. The results identified a total of 41 interconnected dispute factors, which were categorized into three distinct clusters. The application of the ARA revealed several significant associations among these factors, with the primary dispute themes relating to (1) intellectual property and design utilization; (2) contract administration and change management; (3) lack of transparency and qualification issues; (4) negligence and contractual compliance; (5) work quality and remediation; and (6) environmental impact and legal compliance. The findings of this study promote more effective management of green and sustainable construction projects. Ultimately, this provides the associated stakeholders with guidance for proactively mitigating risks and disputes to improve project outcomes.

The fragmented nature of the construction industry has hindered its transition toward circular economy (CE) practices. While game theory (GT) has been successfully applied to support the strategic decisions of CE transitions in other sectors, CE-GT models in the construction domain remain extremely limited. This paper addresses two research questions: (1) how are CE network governance activities currently modeled using GT across domains? (2) How can such models be transferred and adapted to solve strategic interactions within the construction value chain in a manner that enables CE practices to emerge as equilibrium outcomes? These questions are driven by the need to reorganize the construction value chain's decision-making environment to better align with CE principles. To answer these questions, the paper adopts a mixed-methods approach combining deductive content analysis with network analysis. Findings show that market creation games, primarily based on Stackelberg models, are most common and are used for CE pricing mechanisms and profit determination. Evolutionary games follow in frequency, typically applied to evaluate macroscale policies and sociocultural changes. Despite the diversity of models identified, most have been developed for manufacturing-oriented value chains, revealing a need for adaptations to reflect the unique characteristics of the construction sector, such as project-based operations, immobility, uncertain demand, fragmentation, and assembly processes. Several gaps were identified, including the need for modeling workforce education, skill development, and improved integration of policy formulation and implementation in CE transitions. Alongside the proposed adaptations to GT models for the construction sector, future research recommendations include incorporating discount factors, consumption decisions, and learning algorithms to enhance model accuracy and decision-making. This study contributes to the body of knowledge by providing a conceptual framework for integrating GT models into network governance processes, supporting more effective decision-making and policy development for CE implementation within the construction value chain.

Labor productivity is a major concern in the construction industry. Existing research on construction labor productivity (CLP) within specific trades has produced inconsistent findings due to differences in the factors analyzed. This lack of consistency makes it difficult to identify the most critical drivers of productivity losses across trades. To address this gap, this study adopts a cross-trade analytical approach to systematically identify and evaluate the inefficiencies impacting labor productivity in multiple construction trades. Specifically, the study (1) identified common organizational and project-level inefficiencies that influence labor performance; (2) conducted an expert-based survey to measure the frequency and perceived impact of these inefficiencies across key trades; (3) developed a series of extreme gradient boosting (XGBoost) models-one for each trade-to explore the relationship between specific inefficiencies and labor productivity losses; and (4) utilized Shapley additive explanations (SHAP) to interpret the models and identify the most influential productivity-reducing factors. The performance of the XGBoost models was benchmarked against four widely used machine learning algorithms: artificial neural networks (ANN), decision trees (DT), random forest (RF), and gradient-boosted decision trees (GBDT), confirming the robustness of the chosen approach. The results reveal that productivity is trade-sensitive, with different trades facing distinct challenges. For instance, the most critical factors for concreting were "decrease in the proportion of direct work" and "lack of a labor employment system," whereas ironworking was most affected by "drawing errors/lack of drawings" and "high turnover rate." Based on these findings, targeted strategies were proposed under five core themes: (1) labor employment terms, (2) safety culture, (3) skill development, (4) communication, and (5) quality and location of resources. Ultimately, this study provides both trade-specific insights and a holistic perspective on labor productivity, offering practical guidance for informed decision-making in light of ongoing skilled labor shortages in the industry.

As a major input to several work packages, the labor element constitutes a critical component for successful performance of construction projects. Localized labor shortages, fundamental changes in prevailing wage laws, and historical shifts in the unionization rates of construction workers impair the adequate estimation of construction labor costs in diverse labor market dynamics. Meanwhile, existing studies have utilized national-level indicators to study the trends of construction labor costs, but the relationship between the multifaceted local economic factors and state-level construction labor costs remains understudied. This paper fills such a knowledge gap. A three-stage methodology is adopted: (1) data collection of state-level construction labor earnings and macroeconomic indicators as the target variable and predictors, respectively; (2) dimensionality reduction of the state-level macroeconomic indicators using principal component analysis (PCA), and identification of short- and long-term associations between the labor earnings and the macroeconomic indicators using Granger causality and the Johansen cointegration tests; and (3) prediction of the state-level labor earnings using vector error correction (VEC) and long short-term memory (LSTM) recurrent neural network models. The research methodology is demonstrated in the domain of 16 states in the US. Results indicate that in the Northeast states, labor earnings are linked to workforce size and participation rates. In the Midwest and South, inflation indicators consistently precede changes in construction worker earnings, whereas union representation is a reliable indicator of earnings in Illinois, Indiana, and West Virginia. The predictions revealed that multivariate LSTM captures the changes in labor earnings in the long-term forecasting horizons. This study can be replicated to augment the control of labor costs at other geographical domains. The developed multivariate prediction models provide owners and contractors with enhanced state-level estimating of construction labor costs, prescient cost planning in the tendering stage, and proactive control of schedules and budgets during execution.

Price adjustment clauses (PACs) are used by state departments of transportation (DOTs) to share the risk of material price fluctuations. Existing models that depict the price fluctuations of materials forecast a mean within a horizon while relying on the assumption of a constant volatility throughout that horizon. However, none of them examine whether the prices of construction material exhibit similar volatility following a sudden price surge or a price drop. The prospect of PACs and their associated trigger values is hindered in the absence of empirical assessments of their capacity to capture the volatilities in material prices. This paper fills this knowledge gap by adopting a methodology that comprised (1) data collection of the prices of materials; (2) forecasting their trends using autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM); and (3) augmenting the price forecasts with quantification of their asymmetric conditional volatilities using Glosten-Jagannathan-Runkle generalized autoregressive conditional heteroscedasticity (GJR-GARCH). The developed approach was demonstrated using the prices of asphalt, steel, cement, and fuel in Ohio; it can be applied to any other similar data set. Results indicate the existence of significant conditional volatility in the prices of asphalt, steel, and fuel. Further findings suggest that the prices of asphalt and steel are more volatile following a sudden price drop rather than following a price surge. Integration of ARIMA and GJR-GARCH models generates volatility ranges that accurately forecast the fluctuations of material prices in the 12-month forecasting horizon. Out-of-sample forecasts indicated that the GJR-GARCH range constitutes a viable alternative to confidence intervals of competing models. Examining asymmetric conditional volatility in material prices can alleviate the bias in assessing the PACs trigger values following an unstable period. The presented framework aids state DOTs and contractors in their risk sharing strategies in the context of PACs.

Shifting global trade dynamics and the consequent supply chain bottlenecks complicate the accurate estimation of construction material prices. The goal of this paper is to provide reliable predictions of material prices. Although studies have introduced models for forecasting material prices, the stringent assumptions regarding residual properties pose challenges to their robustness. To this end, the authors adopted a multi-step methodology that included developing multivariate long short-term memory (LSTM) models to predict the prices and confidence intervals on the forecasted prices using a distribution-free ensemble batch prediction algorithm. Results indicate that the LSTM models can forecast the price trends of each of the cement, steel, and lumber prices in the US with a mean absolute percentage error of less than 4%. The ensemble batch confidence intervals provide a range that contains the actual prices in the forecasting horizon. The proposed approach helps the stakeholders in making better -informed predictions.

Previous studies consistently highlight a gap in the achievement of Materials and Resources (MR) credits across different versions of the LEED (Leadership in Energy and Environmental Design) rating system, despite its critical role in the transition to a Circular Economy (CE). Furthermore, there is a lack of studies benchmarking the level of the MR credit achievement in the latest version of LEED, v4. This paper examines the integration of CE principles in LEED v4 BD+C (Building Design and Construction): NC (New Construction and Major Renovations), focusing on the 971 certified projects in the US. The study benchmarks the achievement degrees of five MR credits, Building Life-Cycle Impact Reduction (C1), Environmental Product Declarations (C2), Sourcing of Raw Materials (C3), Material Ingredients (C4), and Construction and Demolition Waste Management (C5). The study highlights rather minimal levels of achievement, particularly in credits C1 and C3 where both were fully achieved in only 63 out the 971 studied projects. The findings align LEED certified projects with the lower tier of CE R-frameworks, emphasizing the need for collaboration across project stakeholders, as well as the need for enhanced material data availability to improve CE practices in LEED projects. Ultimately, the study supports discussions on sustainable building practices, advocating for building reuse, optimized life cycle assessments, incentivizing environmental product declarations, and responsible material sourcing.

The US Department of Transportation (USDOT) delivers a wide range of infrastructure projects, backed by a fiscal year 2023 budget exceeding $100 billion. These projects face mounting pressures to meet performance, accountability, and delivery standards, driven by their dependence on public funding and their operational complexity. Transportation infrastructure presents sector-specific challenges-such as time-sensitive user disruptions, multiparty coordination, and asset intersection risks-that demand more robust, automated, and transparent project delivery mechanisms. Blockchain-enabled smart contracts have emerged as a promising solution to address these operational pain points through real-time automation, immutable data records, and decentralized transaction processing. However, the practical realities of the transportation sector-its fragmented systems, regulatory layers, and diverse stakeholder interfaces-create unique integration challenges that remain underexamined. To address this, this study investigates how smart contracts can be effectively integrated into transportation infrastructure by identifying the context-specific needs, requirements, capabilities, and challenges that govern their adoption. A three-phase research design was employed. First, a literature review was conducted to extract generalized integration factors for smart contract use in the broader construction domain. Secondly, these factors were evaluated and ranked by qualified transportation experts to reflect their relevance in sector-specific contexts. Thirdly, structural equation modeling (SEM) was used to analyze expert survey responses and isolate the most influential integration drivers. The results indicate that, unlike general construction projects, the top integration priorities in transportation include (1) compliance checking for quality management (needs); (2) integration with existing cloud repositories or enterprise platforms (requirements); (3) the ability to maintain immutable records (capabilities); and (4) uncertainty regarding usability (challenges). These findings provide a targeted knowledge base for practitioners and policymakers, outlining the critical considerations required for effective and sector-sensitive implementation of smart contracts in transportation infrastructure.

Infrastructure system failures often result from decisions made by key stakeholders responsible for their development and management. Despite the presence of ethical codes designed to prevent such failures, cognitive biases can undermine compliance. Previous research has focused on ethical violations but has overlooked how behavioral ethics constructs drive these breaches. This paper bridges the gap by analyzing an infrastructure system failure case study, then mapping key decisions to violations of the ASCE Code of Ethics and associated behavioral constructs. Following, a graph network analysis quantified the impact and extent of these decisions on project failure. Findings revealed a self-reinforcing cycle of biases such as conformity, slippery slope, and overconfidence, which negatively influenced the decisionmaking network. Ultimately, the study assists professionals, policymakers, and educators to prioritize behavioral ethics interventions to mitigate violations and ultimately prevent future infrastructure failures.

Construction labor shortages constrain project-level objectives and national development plans. The goal of this study is to utilize the lagged effects of macroeconomic conditions as early warning signs of construction labor shortages. To this end, the authors adopted a methodology, encompassing (1) retrieval of publicly available data and preprocessing of construction labor shortage as the target variable and macroeconomic measures as the explanatory variables, (2) identification of short-term associations between shortages and economic cycles using the Granger causality test, (3) examination of long-term relationships between labor shortages and economic conditions using the Johansen cointegration test, and (4) estimation of the impacts of economic conditions on labor shortages using the fixed-effects models. Results show that in the United States and Canada, interest rates and exports are the most significant leading indicators of construction labor shortages, with lags ranging from 12 to 15 months. Panel data analysis in the European Uinon and the United Kingdom reveals that a 1% increase in imports and building permits leads to increases in construction job vacancies by 1.19 and 0.63%, respectively, five quarters later. Findings highlight that by analyzing lagged macroeconomic indicators, construction practitioners can leverage the timely prioritize the strategies to mitigate labor shortages.

The transportation sector's construction workforce faces significant safety risks, with a high rate of injuries influenced by a complex interplay of factors. This study presents a novel methodology to analyze and interpret key factors contributing to safety incidents, using a structured multiple linear regression ( MLR) approach. By integrating top-level data from the Bureau of Labor Statistics, the study examines both internal (e.g., demographic characteristics) and external (e.g., time of day, event type) factors that impact safety. Five high-performing MLR models identify critical combinations of variables linked to incidents, revealing targeted risk profiles within the workforce. This framework addresses data limitations often faced in safety analysis and offers actionable insights for improving safety protocols and reducing injury rates in transportation construction. The findings would empower state Departments of Transportation and industry stakeholders to implement strategic interventions aimed at mitigating specific safety risks and promoting a safer work environment.

The US Department of Transportation oversees complex projects with a substantial budget. Smart contracts, powered by blockchain technology, offer the potential to automate processes, improve efficiency, and enhance transparency. However, unique challenges and integration factors exist for their adoption in the transportation sector. This includes offering security and decentralization to ensure proper accountability of taxpayer money usage. This study aims to identify and prioritize these factors to facilitate the successful implementation of smart contracts in a specific industry. A three-stage methodology was employed, involving a literature review, expert survey, and structural equation modeling. Key findings reveal that the top needs, requirements, capabilities, and challenges were compliance checking to improve construction quality management, integration with existing cloud repositories or enterprise management systems, immutable records, and unknown usability. This research contributes to the understanding of smart contract integration in transportation, providing practical insights for addressing challenges and fostering their adoption.

Private participation bridges the infrastructure investment gap by leveraging private sector expertise and funding. However, infrastructure projects involve multiple stakeholders and carry significant risks, where poor decisions can result in financial losses and system failures. This study examines the World Bank's Private Participation in Infrastructure (PPI) data using topological data analysis (TDA). While prior research has explored predicting project success, little attention has been given to uncovering underlying patterns in the data. Using techniques such as clustering, manifold learning, and TDA mapper, structural features within the data are examined to identify unique characteristics across different project sectors. Findings show projects grouped by differing investment ranges and investment periods, while outliers stemmed from increased investments or inflation. Variance in the data is explained by time-related economic effects followed by investment effects and inflation effects. Failed project s shared common characteristics, linked to macroeconomic shocks, low GDP, and reduced public sector investments.