This study proposes a quantitative and systematic evaluation framework for rationally determining investment priorities in maintenance projects for heterogeneous road infrastructures such as bridges and tunnels. In Korea, conventional maintenance decision-making relies significantly on empirical judgments and policy-driven preferences, thus resulting in inefficiencies, inconsistencies, difficulties across facility types, as well as limited transparency in budget allocation. Hence, a multicriteria decision-making model integrating three key indicators–defect (performance), economic value (asset-based benefit), and risk (safety)–is developed. In particular, the economic evaluation introduces the concept of asset-value recovery and employs artificial intelligence-based machine-learning models (i.e., random forest, light gradient boosting machine, and extreme gradient boosting) to estimate reasonable replacement costs and quantify benefits in monetary terms. The proposed model enables the correlation between these quantitative indicators with maintenance project types to prioritize investments by combining benefit scores and risk indices. The case study demonstrates that the proposed framework enhances the objectivity and efficiency of budget allocation and enables data-driven investment prioritization instead of policydependent decisions. Moreover, this approach provides a foundation for transitioning from experience-based decisions to data-driven infrastructure management. This methodology can be further expanded to include probabilistic risk assessment and life-cycle cost-based management frameworks, thus ultimately contributing to sustainable evidence-based decision support systems for national infrastructure asset management.

As a system complexity increases and technology innovation progresses rapidly, leasing the equipment is considered as an important issue in many engineering areas. In practice, many engineering fields lease the equipment because it is an economical way to lease the equipment rather than to own the equipment. In addition, as the maintenance actions for the equipment are costly and need a specialist, the lessor is responsible for the maintenance actions in most leased contract. Hence, the lessor should establish the optimal maintenance strategy to minimize the maintenance cost. This paper proposes two periodic preventive maintenance policies for the leased equipment. The preventive maintenance action of policy 1 is performed with a periodic interval, in which their intervals are the same until the end of lease period. The other policy is to determine the periodic preventive maintenance interval minimizing total maintenance cost during the lease period. In addition, this paper presents two decision-making models to determine the preventive maintenance strategy for leased equipment based on the lessor’s preference between the maintenance cost and the reliability at the end of lease period. The structural properties of the proposed decision-making model are investigated and algorithms to search the optimal maintenance policy that are satisfied by the lessor are provided. A numerical example is provided to illustrate the proposed model. The results show that a maintenance policy minimizing the maintenance cost is selected as a reasonable decision as the lease term becomes shorter. Moreover, the frequent preventive maintenance actions are performed when the minimal repair cost is higher than the preventive maintenance cost, resulting in higher maintenance cost.

As a system complexity increases and technology innovation progresses rapidly, it tends to lease a system rather than own one. This paper deals with a decision-making model to determine the preventive maintenance strategy for leased equipment. Various maintenance options are presented and formulated via the non-homogeneous Poisson process. During the lease period, the preventive maintenance strategy that minimizes the total cost among the presented maintenance options is selected. A numerical example is provided to illustrate the proposed model.

The operation and management of a plant require proper accounting for the constraints coming from reliability requirements as well as from budget and resource considerations. Most of the mathematical methods to decide the inspection time interval for plan

This study introduces a decision-making support method for maintenance activities of bridges after earthquake. A seismic damage prediction system for bridges which do not have seismic acceleration sensors was developed to support the maintenance activities. The system consists of two main modules, one is seismic load estimation module based on the data from seismic stations and the other is seismic damage prediction modules based on the finite elements analysis models for seismic analysis. After verifying the prediction system for the damaged bridges after Po-Hang Earthquake, it was found that the system was able to predict seismic damages of bridges enough to make a decision of maintenance activities.

가축매몰지 인근 지하수 수질은 지하수 이용과 관련하여 많은 관심이 집중되고 있다. 한국 환경부에서는 지하수 수질 감시를 위하여 지하수 수질을 직접 측정하는 방법을 제시하고 있다. 이 방법은 수질분석에 많은 시간과 비용이 소요되기 때문에 관측지점이 수백개 이상인 경우에는 효율적인 모니터링이 어려울 수 있다. 금회 연구에서는 지하수의 전기 전도도를 매몰지에서의 지하수 수질 지표로 사용하는 방안을 제안하였다. 지하수 수질 수준을 판별하기 위하여 가축 매물지 근방의 장기적인 지하수 수질 측정자료를 통계학적인 방법으로 분석하여 지하수 수질 지표와의 상관관계를 도출하였고, 무선 통신망을 이용한 원격 모니터링 기법을 이용하여 보다 신속하게 지하수 상태를 감시할 수 있는 가능성을 제시하였다.