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

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.

ENERGY ASSESSMENT OF URBAN BUILDINGS BASED ON GEOGRAPHIC INFORMATION SYSTEM

Urban building energy analysis has attracted more attention as the population living in cities increases as does the associated energy consumption in urban environments.This paper proposes a systematic bottom-up method to conduct energy analysis and assess energy saving potentials by combining dynamic engineering-based energy models,machine learning models,and global sensitivity analysis within the GIS(Geographic Information System)environment for large-scale urban buildings.This method includes five steps:database construction of building parameters,automation of creating building models at the GIS environment,construction of machine learning models for building energy assessment,sensitivity analysis for choosing energy saving measures,and GIS visual evaluation of energy saving schemes.Campus buildings in Tianjin(China)are used as a case study to demonstrate the application of the method proposed in this research.The results indicate that the method proposed here can provide reliable and fast analysis to evaluate the energy performance of urban buildings and determine effective energy saving measures to reduce energy consumption of urban buildings.Moreover,the GIS-based analysis is very useful to both create energy models of buildings and display energy analysis results for urban buildings.

Energy characteristics of urban buildings: Assessment by machine learning

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.

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

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.

Resilience-based retrofitting of existing urban RC-frame buildings using seismic isolation

The improvement of the seismic resilience of existing reinforced-concrete(RC) frame buildings, which is essential for the seismic resilience of a city, has become a critical issue. Although seismic isolation is an effective method for improving the resilient performance of such buildings, target-oriented quantitative improvements of the resilient performance of these buildings have been reported rarely. To address this gap, the seismic resilience of two existing RC frame buildings located in a high seismic intensity region of China were assessed based on the Chinese Standard for Seismic Resilience Assessment of Buildings. The critical engineering demand parameters(EDPs) affecting the seismic resilience of such buildings were identified. Subsequently, the seismic resilience of buildings retrofitted with different isolation schemes(i.e., yield ratios) were evaluated and compared, with emphasis on the relationships among yield ratios, EDPs, and levels of seismic resilience. Accordingly, to achieve the highest level of seismic resilience with respect to the Chinese standard, a yield ratio of 3% was recommended and successfully applied to the target-oriented design for the seismic-resilience improvement of an existing RC frame building. The research outcome can provide an important reference for the resilience-based retrofitting of existing RC frame buildings using seismic isolation in urban cities.

Design Strategies for the Sustainable Improvement of Urban Social Assistance Buildings

Transformation and innovation of assistance model promote the transition of urban social assistance building to multi-functional building. This paper, on the basis of analyzing complexity and peculiarity of urban social assistance building, as well as researches and practices of sustainable improvement of domestic architecture, proposed environmental, social and economic objectives of the sustainable improvement of urban social assistance building, and elaborated design strategies from the perspectives of site selection and its impact on environment, application, evaluation and utilization of ecological technologies, reform of old buildings, flexible internal layout of building and combination of routine use and disaster use design, so as to improve social benefits and social assessment of assistance buildings sustainably, and to balance humanistic concerns and social resource input.

The Application and Prospects of Digital Visualization in Urban Public Building Space Design

With the development and innovation of digital information technology, digital visualization plays an increasingly important role in the design of urban public building spaces. This paper explores the application of digital visualization technology in the design of urban public building spaces and looks ahead to future trends. Firstly, it analyzes the challenges in the design of urban public building spaces, including extensive professional involvement, complex functional layout requirements, rational emergency evacuation routes, multidimensional analysis of architectural spatial environments, and appropriate selection of decorative materials. Next, it introduces the applications of digital visualization technology in showcasing visual design and expression, optimizing spatial functional layouts, enhancing the rationality of evacuation routes, analyzing dynamic environmental impacts and energy consumption, and improving the effectiveness of material selection in the design of urban public building spaces. Lastly, it discusses the prospects of extended reality (XR) technology, interactive design using data platforms, and AI technology in the design of public building spaces. It is hoped that this paper provides inspiration and reference for the deeper application of digital information technology in the field of architecture. .

Assessment of Urban Morphology through Local Climate Zone Clas­sification and Detection of the Changing Building States of Siliguri Municipal Corporation and Its Surrounding Area, West Bengal

Progressive population concentration to the urban centres has fuelled urban expansion in both horizontal as well as vertical direction,consequences in the urban landscape change.This growth resulted in posing many complexities towards sustainable urban development which can be counted by observing the changing proportions of natural landscapes and built up areas.Local climate zones(LCZs),a systematic classification of natural lands and built up lands,are identified in Siliguri Municipal Corporation(SMC)and its surrounding region to explore the spatio temporal complexity of urban growth in recent years.Rapid urbanization and population growth of SMC have led to change the building states from low rise to mid and high rise which added an important feature to the urban landscape dynamics of the area.The work intends to provide the vision of spatial urban morphology of the area through investigation of its changing land use and changing urban built space using the LCZ classification.The study shows that the WUDAPT method can accurately generate LCZs,especially the built type LCZs.The results of the proposed LCZ classification scheme are tested using error matrix for the year 2001 and 2021 having coefficient values of 0.79 and 0.81 respectively.The study explores the changing pattern of building states of SMC using LCZ products,which is essential for proper urban planning implementations.

Research on the Development of Housing Industrialization under the New Urbanization

Based on the national strategy of"new urbanization",from the perspective of urban and rural integration,industrial interaction,economy and intensification,this paper proposes to vigorously promote the industrialization planning of urban and rural housing based on SI system,do a good job in the production of building structural parts from the perspective of building industrialization,extend to the internal filling parts,and do a good job in the internal filling system from the perspective of housing industrialization.At the moment of"Rural Revitalization"strategy,we should first develop the SI system multi-storey industrialized housing to meet the needs of rural areas.At the same time,we put forward the development path of China's urban and rural housing industrialization.

Occupancy of rooms in urban residential buildings by users in cold areas of China

Occupancy is used to represent the movements and locations of users among various zones of buildings,and it is the basis of all other daily energy consumption behaviors.This study investigated eight families in cold areas of China based on occupancy measurements obtained in four main rooms,i.e.,living room,bedroom,kitchen,and bathroom.In particular,we analyzed the duration of user occupancy and hourly mean occupancy,and characterized their regular and random features.According to the results,we developed an event-based occupancy model using an inhomogeneous Markov chain,where the rooms were modeled and daily events were divided into three categories according to their randomness.We established a new method for conversion between event characteristic parameters and a transition probability matrix,as well as an overlap avoidance method for active events.The model was then validated using real data.The results showed that the model performed well in terms of two evaluation criteria.The model should improve the accuracy of simulations of occupancy.

Using urban building energy modeling to quantify the energy performance of residential buildings under climate change

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization.Urban building energy modeling(UBEM)is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations,supporting the implementation of carbon emission reduction policies.Currently,most studies focus on the energy performance of archetype buildings under climate change,which is hard to obtain refined results for individual buildings when scaling up to an urban area.Therefore,this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas,by taking two urban neighborhoods comprising 483 buildings in Geneva,Switzerland as case studies.In this regard,GIS datasets and Swiss building norms were collected to develop an archetype library.The building heating energy consumption was calculated by the UBEM tool—AutoBPS,which was then calibrated against annual metered data.A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%.The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways(SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5).The results showed a decrease of 22%–31%and 21%–29%for heating energy consumption,an increase of 113%–173%and 95%–144%for cooling energy consumption in the two neighborhoods by 2050.The average annual heating intensity dropped from 81 kWh/m^(2) in the current typical climate to 57 kWh/m^(2) in the SSP5-8.5,while the cooling intensity rose from 12 kWh/m^(2) to 32 kWh/m^(2).The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7%and 18.6%,respectively,in the SSP scenarios.The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.

Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities:Current trends and future outlook

Energy demand fluctuations due to low probability high impact(LPHI)micro-climatic events such as urban heat island effect(UHI)and heatwaves,pose significant challenges for urban infrastructure,particularly within urban built-clusters.Mapping short term load forecasting(STLF)of buildings in urban micro-climatic setting(UMS)is obscured by the complex interplay of surrounding morphology,micro-climate and inter-building energy dynamics.Conventional urban building energy modelling(UBEM)approaches to provide quantitative insights about building energy consumption often neglect the synergistic impacts of micro-climate and urban morphology in short temporal scale.Reduced order modelling,unavailability of rich urban datasets such as building key performance indicators for building archetypes-characterization,limit the inter-building energy dynamics consideration into UBEMs.In addition,mismatch of resolutions of spatio-temporal datasets(meso to micro scale transition),LPHI events extent prediction around UMS as well as its accurate quantitative inclusion in UBEM input organization step pose another degree of limitations.This review aims to direct attention towards an integrated-UBEM(i-UBEM)framework to capture the building load fluctuation over multi-scale spatio–temporal scenario.It highlights usage of emerging data-driven hybrid approaches,after systematically analysing developments and limitations of recent physical,data-driven artificial intelligence and machine learning(AI-ML)based modelling approaches.It also discusses the potential integration of google earth engine(GEE)-cloud computing platform in UBEM input organization step to(i)map the land surface temperature(LST)data(quantitative attribute implying LPHI event occurrence),(ii)manage and pre-process high-resolution spatio-temporal UBEM input-datasets.Further the potential of digital twin,central structed data models to integrate along UBEM workflow to reduce uncertainties related to building archetype characterizations is explored.It has also found that a trade-off between high-fidelity baseline simulation models and computationally efficient platform support or co-simulation platform integration is essential to capture LPHI induced inter-building energy dynamics.

Study on carbon emission calculation method and carbon reduction indexes of urban building stock

Carbon emissions associated with buildings are a major source of urban emissions. To put forward the methods and strategies to curb carbon emissions from urban building stock, it is not only necessary to establish a carbon emission calculation method for fine statistical analysis, but also to evaluate carbon emissions of urban planning schemes with applicable indexes. Currently,researches mainly focus on carbon emissions of individual buildings. When expanded to urban building stock, the calculation faces the lack of basic data, inadequate spatial analysis and unspecific carbon reduction indexes. Therefore, this study proposes a bottom-up calculation method for urban building stock, conducts spatial analysis based on carbon balance of urban grids, reveals the coupling mechanism between urban carbon reduction indexes and grid carbon emissions, and systematically establishes a carbon-reduction-oriented urban planning method that comprises calculation, analysis and evaluation, which is applied to Xi'an,China. This study provides a theoretical reference for cities to formulate carbon reduction targets and implement planning strategies by evaluating and predicting carbon emissions from urban building stock.

The potential of remote sensing and GIS in urban building energy modelling

As the world continues to urbanize at an unprecedented rate,the energy demand in cities is rising.Buildings account for over 75%of all the energy consumed in cities and are responsible for over two-thirds of the emissions.Assessment of energy demand in buildings is a highly integrative endeavour,bringing together the interdisciplinary fields of energy and urban studies,along with a host of technical domains namely,geography,engineering,economics,sociology,and planning.In the last decade,several urban building energy modelling tools(UBEMs)have been developed for estimation as well as prediction of energy demand in cities.These models are useful in policymaking as they can evaluate future urban energy scenarios.However,data acquisition for generating the input database for UBEM has been a major challenge.In this review,a comprehensive assessment of the potential of remote sensing and GIS techniques for UBEM has been presented.Firstly,the most common input variables of UBEM have been identified by reviewing recent publications on UBEM and then studies related to the acquisition of data corresponding to these variables have been explored.More than 140 research papers and review articles relevant to remote sensing and GIS applications for building level data extraction in urban areas and UBEM applications have been investigated for this purpose.After going through level of details required for each of the input components of UBEM and studying the possibility of acquiring some of those data using remote sensing,it has been inferred that satellite remote sensing and Unmanned Aerial Vehicles(UAVs)have a strong potential in enhancing the input data space for UBEM but their applicability has been limited.Further,the challenges of the usage of these technologies and the possible solutions have also been presented in this study.It is recommended to utilise the existing methodologies of extracting information from remote sensing and GIS for UBEM,along with newer techniques such as machine learning and artificial intelligence.

Research on influencing factors of urban building carbon emissions based on STIRPAT model——taking Suzhou as an example

The research on the influencing factors of carbon emissions from urban buildings is of great significance for the reduction of carbon in the urban building sector and even the realization of the city’s the carbon peak and neutrality goals.In this paper,combined with the ridge regression method,the STIRPAT model is used to establish a new model for influencing factors of building carbon emissions in Suzhou,and the factors such as urbanization rate,the number of permanent residents,per capita construction and tertiary industry added value,and per capita disposable income are analyzed.The analysis results show that the urbanization rate is the primary driving factor for building carbon emissions in Suzhou,followed by the number of permanent residents,then the added value of the per capita construction industry and tertiary industry,and finally the per capita disposable income.The conclusions of this paper indicate that industrialization and urbanization have strongly promoted the growth of building carbon emissions in Suzhou.In the future,with the continuous development of industrialization and urbanization and the increase of population,Suzhou City can rationally plan urban development boundaries to promote green and low-carbon transformation and development in the field of urban and rural construction,improve residents’low-carbon awareness,and advocate green and low-carbon behavior of residents to reduce building carbon emissions.

Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasets

Urban building energy modeling has become an efficient way to understand urban building energy use and explore energy conservation and emission reduction potential.This paper introduced a method to identify archetype buildings and generate urban building energy models for city-scale buildings where public building information was unavailable.A case study was conducted for 68,966 buildings in Changsha city,China.First,clustering and random forest methods were used to determine the building type of each building footprint based on different GIS datasets.Then,the convolutional neural network was employed to infer the year built of commercial buildings based on historical satellite images from multiple years.The year built of residential buildings was collected from the housing website.Moreover,twenty-two building types and three vintages were selected as archetype buildings to represent 59,332 buildings,covering 87.4%of the total floor area.Ruby scripts leveraging on OpenStudio-Standards were developed to generate building energy models for the archetype buildings.Finally,monthly and annual electricity and natural gas energy use were simulated for the blocks and the entire city by EnergyPlus.The total electricity and natural gas use for the 59,332 buildings was 13,864 GWh and 23.6×10^(6) GJ.Three energy conservation measures were evaluated to demonstrate urban energy saving potential.The proposed methods can be easily applied to other cities in China.

Web-based visualization of large 3D urban building models

Adaptive rendering of large urban building models has become an important research issue in three-dimensional(3D)geographic information system(GIS)applications.This study explores a way for rendering web-based 3D urban building models.A client-server hybrid rendering approach is presented for large 3D city models,stored on a remote server through a network.The approach combines an efficient multi-hierarchical building representation with a novel image-based method,3D image impostor generated on demand by a remote server.This approach allows transferring complex scenes progressively while keeping high visualization quality.We also evaluated the rendering and data transferring performance of the proposed approach.

Fitting boxes to Manhattan scenes using linear integer programming

We propose an approach for automatic generation of building models by assembling a set of boxes using a Manhattan-world assumption.The method first aligns the point cloud with a per-building local coordinate system,and then fits axis-aligned planes to the point cloud through an iterative regularization process.The refined planes partition the space of the data into a series of compact cubic cells(candidate boxes)spanning the entire 3D space of the input data.We then choose to approximate the target building by the assembly of a subset of these candidate boxes using a binary linear programming formulation.The objective function is designed to maximize the point cloud coverage and the compactness of the final model.Finally,all selected boxes are merged into a lightweight polygonal mesh model,which is suitable for interactive visualization of large scale urban scenes.Experimental results and a comparison with state-of-the-art methods demonstrate the effectiveness of the proposed framework.