Extreme Storm Surge Return Period Prediction Using Tidal Gauge Data and Estimation of Damage to Structures from Storm-Induced Wind Speed in South Korea

Global warming, which is one of the most serious consequence of climate change, can be expected to have different effects on the atmosphere, the ocean, icebergs, etc. Global warming has also brought secondary consequences into nature and human society directly. The most negative effect among the several effects of global warming is the rising sea level related to the large typhoons which can cause flooding on low-level land, coastal invasion, sea water flow into rivers and underground water, rising river level, and fluctuation of sea tides. It is crucial to recognize surge level and its return period more accurately to prevent loss of human life and property damage caused by typhoons. This study researches two topics. The first purpose of this study is to develop a statistical model to predict the return period of the storm surge water related to typhoon Maemi, 2003 in South Korea. To estimate the return period of the typhoon, clustered separated peaks-over-threshold simulation (CSPS) has been used and Weibull distribution is used for the peak storm surge height’s fitting. The estimated return period of typhoon Maemi’s peak total water level is 389.11 years (95% confidence interval 342.27 - 476.2 years). The second aim is related to the fragility curves with the loss data caused by typhoons. Although previous studies have developed various methods to mitigate damages from typhoons, the extent of financial loss has not been investigated enough. In this research, an insurance company provides their loss data caused by the wind speed of typhoon Maemi in 2003. The loss data is very important in evaluating the extent of the damages. In this study, the damage ratio in the loss dataset has been used as the main indicator to investigate the extent of the damages. The damage ratio is calculated by dividing the direct loss by the insured amount. In addition, this study investigates the fragility curves of properties to estimate the damage from typhoon Maemi in 2003. The damage ratios and storm induced wind speeds are used as the main factor for constructing fragility curves to predict the levels of damage of the properties. The geographical information system (GIS) has been applied to produce properties’ spatial wind speeds from the typhoon. With the damage ratios, wind speeds and GIS spatial data, this study constructs the fragility curves with four different damage levels (Level I - Level IV). The findings and results of this study can be basic new references for governments, the engineering industry, and the insurance industry to develop new polices and strategies to cope with climate change

A study of deep learning algorithm usage in predicting building loss ratio due to typhoons: the case of southern part of the Korean Peninsula

The goal of this study is to suggest an approach to predict building loss due to typhoons using a deep learning algorithm. Due to the influence of climate change, the frequency and severity of typhoons gradually increase and cause exponential destruction of building. Therefore, related industries and the government are focusing their efforts on research and model development to quantify precisely the damage caused by typhoons. However, advancement in the accuracy of prediction is still needed, and the introduction of new technology, obtained due to the fourth revolution, is necessary. Therefore, this study proposed a framework for developing a model based on a deep neural network (DNN) algorithm for predicting losses to buildings caused by typhoons. The developed DNN model was tested and verified by calculating mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2). In addition, to further verify the robustness of the model, the applicability of the framework proposed in this study was verified through comparative verification with the conventional multi-regression model. The results and framework of this study will contribute to the present understanding by suggesting a deep learning method to predict the loss of buildings due to typhoons. It will also provide management strategies to related workers such as insurance companies and facility managers

The Use of BIM in Construction for Decision Making: A Case of Irregular-Shaped Steel-Framed Building Construction Project in South Korea

Building Information Modeling (BIM) is the comprehensive process of developing a computer model of a building project in the phases of designing, analyzing, building, managing, refurbishing and even demolishing the building. Applying BIM to public construction has become an obligation in South Korea. According to the Public Procurement Service in South Korea, the use of BIM has been compulsory on all government projects over $44M since 2012. Moreover, from 2013, the application of BIM will be expanded to all public construction projects over$27.6M. Finally, beginning in 2016, all public construction projects will be required to use BIM. Most research on BIM in South Korea has been focused on developing regulations and policies, application of BIM, solving technical problems, and searching for the value of BIM. However, the use of BIM in Korea during construction for decision-making has not been thoroughly reported in Korea yet. One may be wondering then if BIM is indeed well utilized in Korea during construction for practical decision-making. The objective of this research centers on investigating how a construction company in South Korea is using BIM for its decision making process during ongoing construction phases. For this investigation, a case study method was used. The construction operations on a jobsite in South Korea were monitored June to August in 2012. Field notes were taken to document the decision-making process and information used during field coordination meetings. A total of 36 cases were monitored and recorded. The use of BIM on field was then compared to the industry expectations indicated in the literature. Specifically, the use of BIM for scheduling, estimating, coordination, review of drawings, and tracking for change orders were carefully monitored and compared with the industry expectations as they were addressed in the literature. The results of this research study were mixed. That is, there are not only similarities, but also differences between BIM’s role used for decision making at the construction site, and its expected role described in previous research. The similarities were regarded as minimizing reworking. This factor came to fruition at a construction site by minimizing error, omission in design phases, or congruence in design and construction tasks. These factors could have a positive effect on estimating and scheduling at a construction site. However, use of BIM at a construction site was still limited in reducing repetitive work when 2D drawings were not able to provide enough information to conduct construction. Additionally, even though this study was successful in revealing the connection between the decision making process and the application of BIM at a construction site, the results of the study may not be generalizable to the construction industry as a whole in South Korea. Therefore, further research is needed to ensure its applicability to other construction projects

Toward Multi-Functional Road Surface Design with the Nanocomposite Coating of Carbon Nanotube Modified Polyurethane: Lab-Scale Experiments

A novel multi-functional road surface system is designed to improve safety, the efficiency of traffic flow, and environmental sustainability for future transportation systems. The surface coating, preforming temperature detection with heating element and hydrophobic features, were fabricated with a nanocomposite consisting of carbon nanotube (CNT) modified polyurethane (PU). The CNT/PU coating showed higher electrical conductivity as well as enhanced hydrophobic properties as the CNT concentration increased. The multifunctional properties of CNT/PU coatings were investigated for use in freezing temperature sensing and heating. The CNT/PU coatings showed high temperature sensitivity in the freezing temperature range with a negative temperature coefficient of resistance. In addition, the CNT/PU coatings had excellent heating performance due to the Joule heating effect. Therefore, the proposed CNT/PU coatings are promising for use as multifunctional road coating materials for detection of freezing temperature and deicing by self-heating

Natural Hazard Influence Model of Maintenance and Repair Cost for Sustainable Accommodation Facilities

To optimally maintain buildings and other built infrastructure, the costs of managing them during their entire existence—that is, lifecycle costs—must be taken into account. However, due to technological improvements, developers now build more high-rise and high-performance buildings, meaning that new approaches to estimating lifecycle costs are needed. Meanwhile, an accelerating process of industrialization around the world means that global warming is also accelerating, and the damage caused by natural disasters due to climate change is increasing. However, the costs of losses related to such hazards are rarely incorporated into lifecycle-cost estimation techniques. Accordingly, this study explored the relationship between, on the one hand, some known parameters of natural disasters, such as earthquakes, high winds, and/or flooding, and on the other hand, the data on exceptional maintenance costs, represented by gross loss costs, generated by a large international hotel chain from 2007 to 2017. The regression model used revealed a correlation between heavy rain and insurance-claim payouts. This and other results can usefully inform safety and design guidelines for policymakers, both in disaster management and real estate, as well as in insurance companie