The escalating number of aging sewer pipes necessitates an infrastructure asset management approach to achieve an efficient budget allocation for maintenance. This study suggests a risk-based prioritization framework for sewer pipe inspection considering the predicted condition of sewer pipes and the criticality of the economic, social and environmental impacts associated with them. The results from both models can be used to evaluate the risk of sewer pipes by classification into risk groups. A risk matrix is used for the classification, and it divides the sewer pipes into five risk groups. The results of this study show an improvement in the accuracy of finding sewer pipes in a bad condition using this framework. The condition prediction model can successfully find sewer pipes with a bad condition with over 70% precision. High-risk sewer pipes are highlighted using the differences in the environmental features as well as in the physical features associated with other sewer pipes. Additionally, through the combination of both the condition and criticality of sewer pipes, the framework systemically prioritizes needed maintenance for sewer pipes with a very bad condition. This prioritization framework is expected to help the process of deciding which sewer pipes should be prioritized within a constrained budget.

Interest in clean energy has led to increased international demand for liquefied natural gas (LNG), and the development of LNG plants as megaprojects is also expected to rise. In these megaprojects, the procurement phase is crucial in terms of costs and ripple effects in subsequent phases. If unexpected problems with supply vendors arise, then redesign, reprocurement, and reconstruction are often needed. Although many studies have been conducted on vendor selection, most have focused on identifying evaluation criteria from a general perspective. Existing models are still lacking a comprehensive approach that combines both quantitative and qualitative content to extract the priority level from a pool of candidate supply vendors, particularly for LNG plant projects. Seven evaluation criteria that reflect the uniqueness of LNG plants are identified in the literature and expert interviews. The suggested model can consider multiple scales for each criterion, as well as uncertainty in experts’ opinions,...

Early detection and mitigation of social conflict in civil infrastructure projects is essential due to its significant impact on project performance and social governance. Nevertheless, there is no scientific system for monitoring conflict drivers in a timely manner in practice. Furthermore, previous studies of social conflict in the civil engineering and management domains have relied on manual literature reviews and case studies. Although these qualitative approaches have provided context-specific insights, they are limited in their generalizability and broad perspectives. Against this backdrop, this study presents an automated process for detecting conflict drivers from news articles using ChatGPT. The authors collected news articles related to civil infrastructure projects implemented in the Republic of Korea using web crawling. Then, ChatGPT was used to extract conflict-related keyphrases from the article collections and classify the keyphrases into predefined conflict drivers. The result showed a notable performance with a micro average F1-score of 85.7%. Moreover, the authors confirmed the validity of the keyphrase extraction and classification results through two illustrative case studies. The proposed process and methods contribute to facilitating data-driven conflict management. Although this study focused on conflict drivers of public infrastructure projects, other types of information extraction tasks can benefit from the presented framework.

Despite growing interest in emerging safety technologies in the construction industry, their adoption in practice remains limited due to the lack of structured quantitative models that evaluate the trade-off between safety performance improvements and investment costs. Consequently, it remains unclear under which conditions safety technology adoption can achieve economic justification. To address this limitation, this study proposes the economic evaluation of safety technology adoption (ECOSTAT) framework, which integrates net value calculations, Monte Carlo simulation, and scenario analysis. To account for accident-prevention benefits in this evaluation, this study considers five cost components arising from construction accidents. These components include settlement costs, legal costs, accident investigation costs, interrupted construction costs, and fines. Among them, settlement costs, interrupted construction costs, and fines exhibit substantial variability depending on accident occurrence and characteristics and are therefore modeled using probability distributions. The applicability of the ECOSTAT framework is demonstrated through case studies of two construction project types: a building project and a railway infrastructure project. The results indicate that as total construction costs increase, the minimum accident-prevention efficiency required for economic feasibility decreases, reflecting economies of scale. For example, in the railway project, increasing total construction costs from $200 million to $350 million reduced the required accident-prevention efficiency from 5.36% to 3.95% at an investment ratio of 0.01%. Furthermore, the ECOSTAT framework is implemented as a web-based decision-support system that enables real-time evaluation of economic feasibility by allowing users to adjust project parameters.

The Retrieval-Augmented Generation (RAG) framework has gained attention as a fast and cost-effective method for enhancing the performance of large language models (LLMs). However, its performance remains limited in minority languages such as Korean, and this issue is exacerbated in specialized fields like construction. To address these limitations, this study proposes a dataset construction method that allows low-cost fine-tuning of embedding models originally trained on English-based data. By applying this method in the construction domain, we achieved a top-1 document retrieval accuracy of 58.65%, surpassing the performance of a commercial embedding model provided by OpenAI. We further analyzed how improvements in the embedding model influence the overall RAG pipeline and present both a dataset creation approach and an appropriate evaluation strategy for testing RAG's performance. Our findings suggest that this method can significantly enhance technical efficiency by providing a foundation for diverse language users to effectively utilize RAG in the construction domain.