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Many Korean local governments have enacted ordinances that enable resident participation in the supervision of public construction projects, yet an implementation gap persists between the legal framework and actual engagement. This study thus examined causes of and strategies for residents’ participation in defect reporting and the role of resident supervisor using a sequential embedded design. Administrative data from local governments were analyzed, followed by 94 survey data from resident representatives. Awareness about the defect reporting and role of resident supervisor was low, while support and intention for participation were higher. Awareness, perceived ordinance effectiveness, and support for resident participation were associated with intention, whereas financial rewards showed no significant association. These results suggest that insufficient awareness and trust—not lack of motivation—are the primary barriers, indicating the need to shift from offering rewards to targeted communication, procedural transparency, and capacity-building. This study’s contribution is its mixed-methods empirical assessment of this gap, informing the design of resident-participation policies by prioritizing awareness campaigns, procedural transparency, and training for resident supervisors.

Bid-rigging has long been a problem in the construction area. This research identified the existing problems in evaluating damages from bid-rigging in Design-Bid-Build (DBB) and lowest-bid construction projects. Traditional econometric assessment based on stochastics requires sufficient historical data to obtain reliable estimates. Thus, it is crucial to collect and analyze data from pertinent projects for its utilization. To ensure the accuracy of the estimation, a substantial amount of data on the outcomes of DBB bids should have been gathered. In many construction projects, that requirement could not be met. Furthermore, econometric analysis cannot reflect the differences caused by the details of individual projects. This study proposes an alternative approach based on detailed cost estimates, weights of major work groups and their historical bid rates when historical data from similar projects are scarce. This approach can reflect the distinct characteristics of individual projects. The proposed model was applied to 23 gas pipeline construction projects under DBB and the lowest bid. This study identified the impacts of the different compositions of major work groups. Bid rates of the main work groups could not only be used to evaluate the damages caused by bid-rigging, but could also be used to determine reliable bid prices and provide a bidding strategy to aid the bidder’s decision-making process in future bids for similar projects.

Construction contract review demands specialized expertise, requiring comprehensive understanding of both technical and legal aspects. While AI advancements offer potential solutions, two problems exist: LLMs lack sufficient domain-specific knowledge to analyze construction contracts; existing RAG approaches do not effectively utilize domain expertise. This study aims to develop an automated contract review system that integrates domain expertise with AI capabilities while ensuring reliable analysis. By transforming expert knowledge into a structured knowledge base aligned with the SCF classification, the proposed structured knowledge-integrated RAG pipeline is expected to enable context-aware contract analysis. This enhanced performance is achieved through three key components: (1) integrating structured domain knowledge with LLMs, (2) implementing filtering combined with hybrid dense–sparse retrieval mechanisms, and (3) employing reference-based answer generation. Validation using Oman’s standard contract conditions demonstrated the system’s effectiveness in assisting construction professionals with contract analysis. Performance evaluation showed significant improvements, achieving a 52.6% improvement in Context Recall and a 48.3% improvement in Faithfulness compared to basic RAG approaches. This study contributes to enhancing the reliability of construction contract review by applying a structured knowledge-integrated RAG pipeline that enables the accurate retrieval of expert knowledge, thereby addressing the industry’s need for precise contract analysis.

Most construction projects are delayed, and many are subject to claims or disputes. Therefore, delay analysis is a critical component of any construction project to determine who is responsible for delays. This research examines four different techniques for estimating delay impacts using the impacted as-planned (IAP) method. A sample network was introduced as an example to discuss several concerns. The advantages and limitations of each approach were identified, and recommendations were given for each approach. When inserting an activity or activities representing delay events in IAP, it is necessary to use both constraints and logical relations among delay events, their logical predecessors, and successors. Constraints representing the actual date of delay events are the simplest and easiest. However, constraints should not be used in “single insertion” and “inserting only owner- or contractor-caused delay” approach. In addition, in the case of using constraints, it is critical to ensure that the impact of delay events is less than the duration of those delay events. Constraints should be avoided in this scenario, and delay events should be logically connected to their logical predecessors and successors without constraints. This study also identified through an example that inserting delay events only by logic can cause wrong analysis results. The results of this study will be helpful for delay analysts in identifying what kinds of problems occur in IAP methods and how to prevent those problems.

Amid such international responses to climate change, there is a need to assess environmental loads and to develop an evaluation technique for reduction of greenhouse gas emissions and pollutants in the construction industry. Actual construction projects need to conduct a preliminary evaluation of environmental loads among alternatives in the early design phase. Since current methods are limited by requiring detailed design information, quick decisions are difficult to make. This study is to develop and validate the environmental load estimating model of NATM tunnel based on standard quantity of major works that can estimate the environmental loads by using information available in the early design phase. According to the validation result, the mean absolute error ratio was 3.7%. Moreover, comparing with the conventional basic unit method, it is found that the suggested model was more accurate. When estimating the environmental loads in the early design phase in which available information is limited, the proposed model will be useful to review environmental loads of alternatives in advance.

Urban railway networks have an important role in increasing the mobility of urban populations while decreasing congestion and pollution. Railway network extension that is caused by urban growth requires additional specialists and equipment for the operation, which causes an increase in maintenance cost. A comprehensive review of the literature regarding the application of facility location problem (FLP) in the railway domain found that there are few studies, and most of the current research involves the maintenance of railway vehicles only. In this paper, the purpose of this study is to determine the optimal location of infrastructure maintenance depot (IMD) for storing special vehicles and to support the inspection and maintenance tasks in a timely and efficient manner. For a challenging problem that should reflect the characteristic that traffic load and the deterioration of railway tracks are proportional, we proposed a method of applying maintenance weights to the p-hub median problem. To examine the proposed model, the test network was constructed by collecting information on the Seoul metropolitan subway. The results of case studies show that the number and location of IMDs are significantly affected by maintenance demand and expanded network.

During a long-term construction project, numerous stakeholders participate and collaborate with each other. In such scenario, the processes of creating, storing, and managing project documents are highly complicated. Due to the nature of the construction projects, problems arise in document management such as loss of documents and difficulties in tracking, which can result in claim failure and huge damage. This study proposes a method to efficiently record, search, and manage construction documents that can protect legal rights in preparation for claims/disputes. This study designed a system that can generate, transfer, and synchronize blocks based on email communication whenever an event occurs. It also provides functions such as document search, history tracking, automatic extraction of related document, and authenticity verification for document management. The findings of the study have established that reliability of documents is secured during the recording, storing, and managing processes, and thus claim- and dispute-supporting tasks are supported.

Bid-riggings have persisted as an issue in the construction industry with its estimated damages being the most troublesome element. This study identifies the current limitations in estimating these damages and proposes an alternative approach to estimate the damages from bid-rigging in design-build (DB) construction projects. This study investigated a hypothetical competitive scenario that reflected the pricing model in DB contracts aimed at both winning the project and making profits. The alternative approach utilized a simulation model based on each bidder’s historical bidding data that could reproduce competitive bidding in DB-delivery. The feasibility of the proposed model was demonstrated with a case study conducted on a real railway construction project. This proposed model can also be used in determining an optimal bid price for DB construction projects.

IDentif.AI-x, a clinically actionable artificial intelligence platform, was used to rapidly pinpoint and prioritize optimal combination therapies against COVID-19 by pairing a prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus and Vero E6 assay with a quadratic optimization workflow. A starting pool of 12 candidate drugs developed in collaboration with a community of infectious disease clinicians was first narrowed down to a six-drug pool and then interrogated in 50 combination regimens at three dosing levels per drug, representing 729 possible combinations. IDentif.AI-x revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived, and pinpointed a number of clinically actionable drug interactions, which were further reconfirmed in SARS-CoV-2 variants B.1.351 (Beta) and B.1.617.2 (Delta). IDentif.AI-x prioritized promising drug combinations for clinical translation and can be immediately adjusted and re-executed with a new pool of promising therapies in an actionable path towards rapidly optimizing combination therapy following pandemic emergence.

Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak.