• Wind and Structures
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• 제36권6호
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• pp.355-366
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• 2023

Although machine learning (ML) techniques have been widely used in various fields of engineering practice, their applications in the field of wind engineering are still at the initial stage. In order to evaluate the feasibility of machine learning algorithms for prediction of wind loads on high-rise buildings, this study took the exposure category type, wind direction and the height of local wind force as the input features and adopted four different machine learning algorithms including k-nearest neighbor (KNN), support vector machine (SVM), gradient boosting regression tree (GBRT) and extreme gradient (XG) boosting to predict wind force coefficients of CAARC standard tall building model. All the hyper-parameters of four ML algorithms are optimized by tree-structured Parzen estimator (TPE). The result shows that mean drag force coefficients and RMS lift force coefficients can be well predicted by the GBRT algorithm model while the RMS drag force coefficients can be forecasted preferably by the XG boosting algorithm model. The proposed machine learning based algorithms for wind loads prediction can be an alternative of traditional wind tunnel tests and computational fluid dynamic simulations.

• 한국지진공학회논문집
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• 제20권7_spc호
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• pp.485-494
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• 2016

The risk-based assessment, also called time-based assessment of structure is usually performed to provide seismic risk evaluation of a target structure for its entire life-cycle, e.g. 50 years. The prediction of collapse probability is the estimator in the risk-based assessment. While the risk-based assessment is the key in the performance-based earthquake engineering, its application is very limited because this evaluation method is very expensive in terms of simulation and computational efforts. So the evaluation database for many archetype structures usually serve as representative of the specific system. However, there is no such an assessment performed for building stocks in Korea. Consequently, the performance objective of current building code, KBC is not clear at least in a quantitative way. This shortcoming gives an unresolved issue to insurance industry, socio-economic impact, seismic safety policy in national and local governments. In this study, we evaluate the comprehensive seismic performance of an low-rise residential buildings with discontinuous structural walls, so called piloti-type structure which is commonly found in low-rise domestic building stocks. The collapse probability is obtained using the risk integral of a conditioned collapse capacity function and regression of current hazard curve. Based on this approach it is expected to provide a robust tool to seismic safety policy as well as seismic risk analysis such as Probable Maximum Loss (PML) commonly used in the insurance industry.