Assessing the Risk of Natural Disaster-Induced Losses to Tunnel-Construction Projects Using Empirical Financial-Loss Data from South Korea

Yum, Sang-Guk; Ahn, Sungjin; Bae, Junseo; Kim, Ji-Myong

doi:10.3390/su12198026

Cited by 21 publications

(21 citation statements)

References 56 publications

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“…The DNN model revealed that natural disasters could be associated with financial losses. This finding can be utilized for mitigating unexpected risks such as financial losses since natural disasters would be the strongest influential factors for losses at construction projects [13]. Future construction projects can utilize findings of this study to cope with various possible natural hazards for preventing unexpected financial losses while considering additional more specific construction site-oriented variables such as geographical and local weather.…”

Section: Discussionmentioning

confidence: 81%

“…Considering that the ultimate goal of risk analysis is the efficient allocation and input of defined resources, quantitative methodology is more suitable for risk analysis. In addition, the quantitative methodology enables a more realistic and accurate analysis of the potential risks of construction sites and enables higher generalization [13]. Furthermore, through the introduction of a quantitative risk analysis methodology, potential risk factors can be identified, factors indexed, and quantified models can be developed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many studies have been conducted to find valuable factors for estimating damages caused by natural hazards. Yum et al [13] have proposed a methodology to find the most critical factors affecting tunnel construction projects. They utilized various atmosphere factors such as wind speed, rainfall, and so on to compare the actual amount of loss provided by insurance companies.…”

Section: Literature Reviewmentioning

confidence: 99%

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Development of Model to Predict Natural Disaster-Induced Financial Losses for Construction Projects Using Deep Learning Techniques

et al. 2021

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This study goals to develop a model for predicting financial loss at construction sites using a deep learning algorithm to reduce and prevent the risk of financial loss at construction sites. Lately, as the construction of high-rise buildings and complex buildings increases and the scale of construction sites surges, the severity and frequency of accidents occurring at construction sites are swelling, and financial losses are also snowballing. Singularly, as natural disasters rise and construction projects in urban areas increase, the risk of financial loss for construction sites is mounting. Thus, a financial loss prediction model is desired to mitigate and manage the risk of such financial loss for maintainable and effective construction project management. This study reflects the financial loss incurred at the actual construction sites by collecting claim payout data from a major South Korean insurance company. A deep learning algorithm was presented in order to develop an objective and scientific prediction model. The results and framework of this study provide critical guidance on financial loss management necessary for sustainable and successful construction project management and can be used as a reference for various other construction project management studies.

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Section: Discussionmentioning

confidence: 81%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Development of Model to Predict Natural Disaster-Induced Financial Losses for Construction Projects Using Deep Learning Techniques

et al. 2021

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“…The construction vulnerability reflects the different resistance of different railway constructions with respect to specific geohazards [44]. To estimate the construction vulnerability, a simple but effective score ranking method is proposed.…”

Section: Spatial Geohazard Assessment 331 Spatial Construction Vulmentioning

confidence: 99%

Railway Alignment Optimization in Mountainous Regions Considering Spatial Geological Hazards: A Sustainable Safety Perspective

Xie

Schonfeld

et al. 2021

Sustainability

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Sustainable railway construction and operation are threatened by densely occurring geological hazards in complex mountainous regions. Thus, during the alignment optimization process, it is vital to reduce the harmful impacts of geological hazards to a railway. However, current alignment-related studies solely consider such threats in existing geological hazard regions and, outside these regions, slight attention has been devoted to the assessment of potential hazardous impacts along the alignment. To this end, this paper proposes a novel railway alignment optimization model considering both existing and potential geological hazards based on quantitative geological hazard evaluation criteria from a sustainable safety perspective. More specifically, a geohazard zone classification method, within which an energy–slope model is integrated, is first developed. Three geohazard regions, namely the geohazard outbreak region, buffer region and fuzzy region, can then be obtained. Afterward, a spatial geological hazard assessment model is constructed considering the geological danger of three kinds of geohazards (debris flows, landslides and rockfalls) and railway construction vulnerability. This model is incorporated into a previous cost–hazard bi-objective alignment optimization model. Finally, the effectiveness of the proposed model is verified by applying it to a real-life case of the Sichuan–Tibet railway. The results show that this method can effectively optimize mountain railway alignments by concurrently reducing geological hazards and costs, which is beneficial to railway safety and sustainable construction and operation.

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“…For the quantitative and objective assessment of natural hazards, the risk rating of the Munich Reinsurance Company's Natural Risk Assessment Network (NATHAN) was adopted. NATHAN was developed through a comprehensive analysis of public sources, scientific analyses, and the severity and frequency of past natural hazards, aimed at estimating the risk of natural hazards around the world through the risk of various natural hazards according to geographical locations [23]. The risk rating of each natural disaster was used as the nominal variable.…”

Section: Maintenance and Repair Cost Ratio =mentioning

confidence: 99%

Modeling Deep Neural Networks to Learn Maintenance and Repair Costs of Educational Facilities

et al. 2021

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Educational facilities hold a higher degree of uncertainty in predicting maintenance and repair costs than other types of facilities. Moreover, achieving accurate and reliable maintenance and repair costs is essential, yet very little is known about a holistic approach to learning them by incorporating multi-contextual factors that affect maintenance and repair costs. This study fills this knowledge gap by modeling and validating deep neural networks to efficiently and accurately learn maintenance and repair costs, drawing on 1213 high-confidence data points. The developed model learns and generalizes claim payout records on the maintenance and repair costs from sets of facility asset information, geographic profiles, natural hazard records, and other causes of financial losses. The robustness of the developed model was tested and validated by measuring the root mean square error and mean absolute error values. This study attempted to propose an analytical modeling framework that can accurately learn various factors, significantly affecting the maintenance and repair costs of educational facilities. The proposed approach can contribute to the existing body of knowledge, serving as a reference for the facilities management of other functional types of facilities.

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Assessing the Risk of Natural Disaster-Induced Losses to Tunnel-Construction Projects Using Empirical Financial-Loss Data from South Korea

Cited by 21 publications

References 56 publications

Development of Model to Predict Natural Disaster-Induced Financial Losses for Construction Projects Using Deep Learning Techniques

Development of Model to Predict Natural Disaster-Induced Financial Losses for Construction Projects Using Deep Learning Techniques

Railway Alignment Optimization in Mountainous Regions Considering Spatial Geological Hazards: A Sustainable Safety Perspective

Modeling Deep Neural Networks to Learn Maintenance and Repair Costs of Educational Facilities

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