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Seismic vulnerability assessment of urban buildings using the rough set theory and weighted linear combination

Journal of Mountain Science 2022年第3期19卷 849-861页

作者： Yasaman ASADI Najmeh NEYSANI SAMANY Majid KIAVARZ MOQADAM Ata ABDOLLAHI KAKROODI Meysam ARGANY Department of Remote Sensing and GIS Faculty of GeographyUniversity TehranTehran 33137-67464Iran

Seismic vulnerability assessment of urban buildings is among the most crucial procedures to post-disaster response and recovery of infrastructure systems.The present study proceeds to estimate the seismic vulnerability of urban buildings and proposes a new framework training on the two objectives.First,a comprehensive interpretation of the effective parameters of this phenomenon including physical and human factors is done.Second,the Rough Set theory is used to reduce the integration uncertainties,as there are numerous quantitative and qualitative data.Both objectives were conducted on seven distinct earthquake scenarios with different intensities based on distance from the fault line and the epicenter.The proposed method was implemented by measuring seismic vulnerability for the seven specified seismic scenarios.The final results indicated that among the entire studied buildings,71.5%were highly vulnerable as concerning the highest earthquake scenario(intensity=7 MM and acceleration calculated based on the epicenter),while in the lowest earthquake scenario(intensity=5 MM),the percentage of vulnerable buildings decreased to approximately 57%.Also,the findings proved that the distance from the fault line rather than the earthquake center(epicenter)has a significant effect on the seismic vulnerability of urban buildings.The model was evaluated by comparing the results with the weighted linear combination(WLC)method.The accuracy of the proposed model was substantiated according to evaluation reports.Vulnerability assessment based on the distance from the epicenter and its comparison with the distance from the fault shows significant reliable results.

关键词： Seismic vulnerability urban buildings Rough set theory Earthquake scenarios WLC

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Energy characteristics of urban buildings: Assessment by machine learning

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Building Simulation 2021年第1期14卷 179-193页

作者： Wei Tian Chuanqi Zhu Yu Sun Zhanyong Li Baoquan Yin Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry&Food Machinery and Equipment College of Mechanical EngineeringTianjin University of Science and TechnologyTianjin300222China Tianjin International Joint Research and Development Center of Low-Carbon Green Process Equipment Tianjin300222China School of Architecture Harbin Institute of TechnologyHarbin150090China Tianjin Architecture Design Institute Tianjin300074China

Machine learning techniques have attracted more attention as advanced data analytics in building energy analysis.However,most of previous studies are only focused on the prediction capability of machine learning algorithms to provide reliable energy estimation in buildings.Machine learning also has great potentials to identify energy patterns for urban buildings except for model prediction.Therefore,this paper explores energy characteristic of London domestic properties using ten machine learning algorithms from three aspects:tuning process of learning model;variable importance;spatial analysis of model discrepancy.The results indicate that the combination of these three aspects can provide insights on energy patterns for urban buildings.The tuning process of these models indicates that gas use models should have more terms in comparison with electricity in London and the interaction terms should be considered in both gas and electricity models.The rankings of important variables are very different for gas and electricity prediction in London residential buildings,which suggests that gas and electricity use are affected by different physical and social factors.Moreover,the importance levels for these key variables are markedly different for gas and electricity consumption.There are much more important variables for electricity use in comparison with gas use for the importance levels over 40.The areas with larger model discrepancies can be determined using the local spatial analysis based on these machine learning models.These identified areas have significantly different energy patterns for gas and electricity use.More research is required to understand these unusual patterns of energy use in these areas.

关键词： urban buildings energy characteristics machine learning variable importance spatial analysis

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Energy and carbon performance of urban buildings using metamodeling variable importance techniques

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Building Simulation 2021年第3期14卷 535-547页

作者： Yunliang Liu Wei Tian Xiang Zhou School of Mechanical Engineering Tongji UniversityShanghai 200092China Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry&Food Machinery and Equipment College of Mechanical EngineeringTianjin University of Science and TechnologyTianjin 300222China Tianjin International Joint Research and Development Center of Low-Carbon Green Process Equipment Tianjin 300222China

Global urbanization causes more environmental stresses in cities and energy efficiency is one of major concerns for urban sustainability.The variable importance techniques have been widely used in building energy analysis to determine key factors influencing building energy use.Most of these applications,however,use only one type of variable importance approaches.Therefore,this paper proposes a procedure of conducting two types of variable importance analysis(predictive and variance-based)to determine robust and effective energy saving measures in urban buildings.These two variable importance methods belong to metamodeling techniques,which can significantly reduce computational cost of building energy simulation models for urban buildings.The predictive importance analysis is based on the prediction errors of metamodels to obtain importance rankings of inputs,while the variance-based variable importance can explore non-linear effects and interactions among input variables based on variance decomposition.The campus buildings are used to demonstrate the application of the method proposed to explore characteristic of heating energy,cooling energy,electricity,and carbon emissions of buildings.The results indicate that the combination of two types of metamodeling variable importance analysis can provide fast and robust analysis to improve energy efficiency of urban buildings.The carbon emissions can be reduced approximately 30%after using a few of effective energy efficiency measures and more aggressive measures can lead to the 60%of reduction of carbon emissions.Moreover,this research demonstrates the application of parallel computing to expedite building energy analysis in urban environment since more multi-core computers become increasingly available.

关键词： urban buildings variable importance metamodeling energy performance carbon emissions

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