Assuring quality management is essential for a project’s success for a specific price and within a specific time period. With an increasing number of highway projects being delivered by the design-build (DB) project delivery system, conducting quality assurance (QA) programs represents new challenges for state departments of transportation (DOTs) in the United States, which leads to the variation in quality management practice across the states. Despite those changes, achieving outstanding quality is still one of the most important goals of highway agencies and ultimate road users. This study examines the state of practices in quality management to achieve the main objective, which is to capture the underpinning of materials and workmanship quality management as utilized by DOTs in the development of DB highway projects. This study employs a content analysis methodology that involves reviewing literature, federal regulations, quality manuals, and state DOT solicitation documents. To obtain a deeper understanding, structured interviews of DOT personnel and industry experts were conducted. The findings of this research consisted of two major parts: roles and responsibilities of the stakeholders in the QA process, and the associated cost mechanisms to fund it. Under these parts, five key issues in practical components of quality management in DB highway programs were identified: (1) agency acceptance approaches; (2) nonconformance reports; (3) selection criteria; (4) QA budgeting mechanism; and (5) pay adjustment factors. Some DOTs use a consistent approach to QA management, and other DOTs tend to change their approach based on their experience in implementing QA program. DOTs continue using similar QA practices with the traditional design-bid-build method or adjust their practices in compliance with the DB environment. The major contributions of this research to the body of knowledge are identifying major issues associated with QA for DB highway projects and determining differences in practical components of quality management currently in use by various DOTs in their DB programs.

In the integrated environment of design-build (DB), design risk is transferred from the owner to the DB team. One significant challenge to DB projects is design liability. This study discusses important challenges related to design professional liability issues in the transportation DB industry. This study developed a national survey and distributed it to a wide range of subject-matter experts from multiple disciplines: design consultants, highway contractors, public owners, owner representatives, and insurance and legal advisors. The study identified two broad areas of challenges for design liability: design claim issues and heightened standards of care. The results show that industry professionals hold different and often conflicting perceptions regarding the severity of design liability issues. Subject-matter experts from various industry stakeholder groups expressed different viewpoints regarding the increased number of design claims in transportation DB projects. They had considerably different perceptions regarding the importance of significant design sources in the DB environment. It was found that language on heightened standard of care can be identified more frequently in DB projects than in traditional design-bid-build projects. This research was the first national study to identify design liability issues in the context of the transportation DB industry. Its findings contribute to a better understanding of key challenges in design liability and provide insights into and opportunities for decision makers to reduce design liability issues.

Purpose: This study aims to analyze low bid deviations in transportation infrastructure projects, identify key project and market-related factors influencing these deviations, and employ a machine learning (ML) framework to improve bid accuracy and forecasting. Design/methodology/approach: This study analyzes 2,915 historical transportation project bids from Louisiana (2012–2024). The dataset was split into training (80%) and testing (20%) subsets. A two-stage feature selection process was implemented, combining Pearson correlation filtering and mutual information ranking to select the top nine predictive variables. Five ML models, LightGBM, CatBoost, XGBoost, TabNet, and a stacking ensemble, were trained and evaluated. Model interpretability was enhanced through SHapley Additive exPlanations (SHAP) and sensitivity analysis. Findings: TabNet achieved the best predictive performance, capturing complex, nonlinear relationships among variables. SHAP analysis revealed that the number of bidders, construction investment levels, and material costs were the most influential factors. Sensitivity analysis confirmed the measurable impact of construction demand and input prices on predicted deviations, while employment-related variables showed lower marginal influence. Research limitations/implications: This study is limited by its reliance on historical bid data from a single U.S. state (Louisiana), which may constrain the generalizability of the findings to other regions with different procurement frameworks, contractor behaviors, and economic dynamics. Furthermore, while the selected ML models demonstrated strong predictive performance under typical market conditions, their effectiveness may decrease during periods of economic volatility or unexpected disruption. These limitations highlight the need for future research to incorporate multi-state datasets, consider temporal modeling strategies, and explore regional heterogeneity in contractor markets and procurement practices. Such expansions would help validate and enhance the robustness of ML-based frameworks in broader public infrastructure contexts. Practical implications: The results underscore the practical value of using ML-based tools, such as TabNet, in transportation cost estimation and procurement planning. By identifying key predictors of bid deviations—such as competition levels, construction demand, and material prices—transportation agencies can refine early-stage cost estimates and reduce estimation bias. Additionally, using interpretable models with SHAP and sensitivity analysis empowers decision-makers with actionable insights, promoting transparency and informed procurement strategies. Encouraging broader contractor participation and integrating real-time market indicators into cost estimation workflows can ultimately enhance project planning accuracy and procurement efficiency. Originality/value: This research presents a replicable, interpretable ML framework for analyzing low bid deviations in public infrastructure projects. The approach enables transportation agencies to better understand the drivers of bid variability, improve cost estimation practices, and adapt procurement strategies to evolving market conditions.

Achieving exemplary quality standards on delivered projects is essential for the State DOTs as this presents a direct reflection of the agency’s performance. A study conducted by DBIA shows that transportation is the fastest-growing design-build sector in the United States, with a 600% increase in the number of projects completed since 2002. The owner and the design-builder share the liability of performance in design-build projects. As a result, owner is concerned over the loss of direct control on managing quality. Additionally, DOT staffing levels have been on the decline due to barriers in hiring. In light of these developments, new challenges have emerged when it comes to managing quality assurance on delivered projects. The main objective of this study is to provide a synthesis of quality assurance (QA) practices in different state DOTs for alternative delivery projects. Our research methodology involves a two-step process. We performed a thorough content analysis on the latest quality management guidelines suggested by the FHWA and analyzed the variations in the current state-of-practice of a spectrum of DOTs across the country. To get a deeper understanding of critical issues and answers to specific questions, we conducted structured interviews of industry experts. Results indicate that more and more responsibilities of quality assurance are being transferred to design-builder teams. While the roles concerning quality control are fairly similar across all DOTs, a wide variation is observed in the quality acceptance roles. The main contribution of this research is identification of important challenges in quality assurance along with innovative approaches to overcome them.