Sustainable disaster recovery involves not merely the restoration of the physical built environment, but also meeting the needs of the impacted stakeholders and decreasing the vulnerability of the host community to future hazards. Nonetheless, the available disaster recovery frameworks deal with community restoration as isolated redevelopment projects and lack the integration of the host community’s overall welfare and vulnerability into the objective functions of the optimization models. This paper balances the short-term redevelopment objectives and the long-term goals of reducing the host community three-dimensional vulnerability (i.e., social, economic, and environmental). The authors develop an innovative decision-making framework that assimilates and meets the needs of the broad community stakeholders. Through a bottom-up multiagent-based model, the authors simulate the stakeholders’ decision-making processes and integrate well-established vulnerability indicators into the objective functions of the stakeholders to better guide the redevelopment strategies. Adopting the recovery processes in the aftermath of Hurricane Katrina catastrophe, the proposed model is implemented and tested on the residential housing and economic financial recovery as well as the infrastructure development in three devastated coastal counties in Mississippi. Each action plan provided by governmental agencies had its own advantages and negative impacts on the vulnerability and/or the recovery of the impacted communities. By holistically addressing the multidimensional recovery processes using simultaneous simulation and optimization techniques, the model develops a Pareto optimal set of strategies that increases the recovery rates and decreases the three-dimensional vulnerabilities. The proposed innovative approach gives decision makers a broader understanding of the disaster recovery outcomes rather than relying on strategies that aim to return the community to pre-event conditions. As such, the proposed holistic approach will enable decision makers to identify optimal disaster recovery strategies that achieve immediate redevelopment objectives and avoid sacrificing the right of future generations to sustainability and resilience.

Timely approvals and payments to the project participants are crucial for successful completion of construction projects. However, the construction industry faces persistent delays and non-payments to contractors. Despite the desirable benefits of automated payments and enhanced access to digitized data progress, most payment applications rely on centralized control mechanisms; inefficient procedures; and documentation that takes time to prepare, review, and approve. As such, there is a need for a reliable payment automation system that guarantees timely execution of payments upon the detection of completed works. Therefore, this study used a cutting-edge approach to automate construction payments by integrating blockchain-enabled smart contracts and scan-to-Building Information Modeling (BIM). In this approach, scan-to-BIM provides accurate, real-time building progress data, which serve as the source of verifiable off-chain data. A chain-link is then used to securely relay these data to the blockchain system. Blockchain-enabled smart contracts automate the execution of payments upon meeting contract conditions. The proposed approach was implemented on a real case study project. The actual site scan was captured using a photogrammetry 360° camera, which uses a combination of structured light and infrared depth sensing technology to capture 3D data and create detailed 3D models of spaces. This study leveraged accurate, real-time building progress data to automate payments using blockchain-enabled smart contracts upon work completion, thus reducing payment disputes by tying payments to verifiable construction progress, leading to faster release of payments. The findings show that this approach provides a transparent basis for payment, enhancing trust and allowing precise project progress tracking.

Construction joint ventures (CJVs) execute business by pooling diverse technical and financial contributions from collaborating entities. Traditional CJV profit-allocation approaches account only for investment shares, and do not address the marginal contribution of the participating parties. Therefore, disagreements may arise between stakeholders. This research aims to reduce profit-share-related disagreements among multiple CJV members by allocating profit based on the marginal contribution of each party. The authors developed a conceptual framework using the Shapley value as an alternative to the traditional investment-based approach. Three illustrative examples demonstrated the possible use of the developed conceptual framework. Results of the study highlighted the potential of Shapley value as an alternative profit allocation scheme. The stability of the generated results was validated mathematically, and decision makers’ perception of fairness was addressed following the methods of prior experimental cooperative game theory research. This paper contributes to the body of knowledge by proposing an axiomatically fair methodology for profit-sharing negotiations among multiple collaborating parties in a project. This approach can be utilized in other engineering domains where the management needs to foster stable and fair collaborations among its stakeholders.

This paper presents a simultaneous multi-criteria optimization approach for scheduling linear infrastructure projects. The proposed model provides planners with sets of non-dominated alternatives and their corresponding tradeoffs. The associated research methodology includes: (1) developing a resource driven scheduling module; (2) applying a multi-criteria optimization technique to optimize the multi-objective scheduling problem; (3) integrating the proposed model with a commercial project management software; and (4) applying the developed model on two literature-drawn case studies. The developed multi-criteria optimization approach utilizes Genetic Algorithms and Pareto Front sorting. The resulting sets of schedules are based on the multiple inter-conflicting objectives of simultaneously minimizing project duration, minimizing cost, minimizing interruptions, and minimizing unit delivery delays. The results indicate that the proposed approach can explore a greater range of solutions compared to existing models. The developed multi-criteria optimization approach can aid planners with proposing optimal set of schedules.

Concurrent delays are multiple project participants incurring delays on parallel schedule paths. They have been controversial because it is challenging to apportion liability for liquidated damages fairly and equitably. To date, there exist neither a transparent solution, nor one that addresses all possible scenarios, including mixed, where some participants are ahead of schedule and others are behind. This study therefore proposes a comprehensive model that also newly includes incentive bonuses. This cooperative game theory–based approach has three steps: (1) identify as-planned and as-built schedule inputs; (2) categorize delayed and ahead activities; and (3) operate games to determine Shapley values. Compared with prior delay analysis methods, our model is more robust and apportions liability not just in days, but also in financial terms. A real-world case was analyzed to validate it and demonstrate the reasonableness of its results. This study contributes to the body of knowledge by creating an objective concurrent delay analysis using readily available project data. It also aids in incentivizing project participants to deliver their best possible performance.