3D Building Structures Research Articles

GLAMOUR: GLobAl building MOrphology dataset for URban hydroclimate modelling

Understanding building morphology is crucial for accurately simulating interactions between urban structures and hydroclimate dynamics. Despite significant efforts to generate detailed global building morphology datasets, there is a lack of practical solutions using publicly accessible resources. In this work, we present GLAMOUR, a dataset derived from open-source Sentinel imagery that captures the average building height and footprint at a resolution of 0.0009° across urbanized areas worldwide. Validated in 18 cities, GLAMOUR exhibits superior accuracy with median root mean square errors of 7.5 m and 0.14 for building height and footprint estimations, indicating better overall performance against existing published datasets. The GLAMOUR dataset provides essential morphological information of 3D building structures and can be integrated with other datasets and tools for a wide range of applications including 3D building model generation and urban morphometric parameter derivation. These extended applications enable refined hydroclimate simulation and hazard assessment on a broader scale and offer valuable insights for researchers and policymakers in building sustainable and resilient urban environments prepared for future climate adaptation.

Sat2Map: Reconstructing 3D Building Roof from 2D Satellite Images

Three-dimensional (3D) urban models have gained interest because of their applications in many use cases, such as disaster management, energy management, and solar potential analysis. However, generating these 3D representations of buildings require lidar data, which is usually expensive to collect. Consequently, the lidar data are not frequently updated and are not widely available for many regions in the US. As such, 3D models based on these lidar data are either outdated or limited to those locations where the data is available. In contrast, satellite images are freely available and frequently updated. We propose sat2Map , a novel deep learning-based approach that predicts building roof geometries and heights directly from a single 2D satellite image. Our method first uses sat2pc to predict the point cloud by integrating two distinct loss functions, Chamfer Distance and Earth Mover’s Distance, resulting in a 3D point cloud output that balances overall structure and finer details. Additionally, we introduce sat2height , a height estimation model that estimates the height of the predicted point cloud to generate the final 3D building structure for a given location. We extensively evaluate our model on a building roof dataset and conduct ablation studies to analyze its performance. Our results demonstrate that sat2Map consistently outperforms existing baseline methods by at least 18.6%. Furthermore, we show that our refinement module significantly improves the overall performance, yielding more accurate and fine-grained 3D outputs. Our sat2height model demonstrates a high accuracy in predicting height parameters with a low error rate. Furthermore, our evaluation results show that we can estimate building heights with a median mean absolute error of less than 30 cm while still preserving the overall structure of the building.

An Algorithm for Building Exterior Facade Corner Point Extraction Based on UAV Images and Point Clouds

The high-precision building exterior facade corner point (BEFCP) is an essential element in topographic and cadastral surveys. However, current extraction methods rely on the interactions of humans with the 3D real-scene models produced by unmanned aerial vehicle (UAV) oblique photogrammetry, which have a high workload, low efficiency, poor precision, and cannot satisfy the requirements of automation. The dense point cloud contains discrete 3D building structure information. Still, it is challenging to accurately filter out the partial point cloud characterizing the building structure from it in order to achieve BEFCP extraction. The BEFCPs are always located on the plumb line of the building’s exterior wall. Thus, this paper back-calculated the plumb line from the image and designed a photographic ray corresponding to the image point and point cloud intersection point calculation algorithm to recover its approximate spatial position in order to successfully extract the accurate point cloud in the building structure neighborhood. It then utilized the high signal-to-noise ratio property of the point cloud as a base to eliminate the noise points and, finally, accurately located the building exterior façade corner points by recovering the building structure through segmental linear fitting of the point cloud. The proposed algorithm conducted automated building exterior facade corner point extraction via both of planar-to-stereo and rough-to-precise strategies, reached a 92.06% correctness rate and ±4.5 cm point mean square location error in the experiment, and was able to extract and distinguish the building exterior facade corner points under eaves obstruction and extreme proximity. It is suitable for all high-precision surveying and mapping tasks in building areas based on oblique photogrammetry, which can effectively improve the automation of mapping production.

3DCentripetalNet: Building height retrieval from monocular remote sensing imagery

Three-dimensional (3D) building structures are vital to understanding urban dynamics. Monocular remote sensing imagery is a cost-effective data source for large-scale building height retrieval when compared to LiDAR data and multi-view imagery. Existing methods learn building footprints and height maps per pixel via either a multi-task network or two separate networks, however, failing to consider the information of neighboring pixels that belong to the identical building. Therefore, we propose learning a novel representation for 3D buildings, namely 3D centripetal shifts, a unified representation of individual building instances. Our method is termed as 3DCentripetalNet and learns the 3D centripetal shift representation that incorporates planar and vertical structures of buildings. Afterward, a decoupling module is devised to learn building corner points. Finally, a 3D modeling module is designed to retrieve building height from the learned 3D centripetal shift map and corner points. We investigate the proposed 3DCentripetalNet on two datasets with different spatial resolutions, i.e., the ISPRS Vaihingen dataset (9 cm/pixel) and the Urban 3D dataset (50 cm/pixel). Experimental results suggest that 3DCentripetalNet is able to preserve sharp building boundaries, largely alleviate false detections, and significantly outperform other competitors. Thus, we believe that 3DCentripetalNet is a robust solution for the task of building height retrieval from monocular imagery.

Seasonally disparate responses of surface thermal environment to 2D/3D urban morphology

The driving mechanisms of urban surface thermal environments usually differ with season. However, to date, the seasonal differences in the impacts of two-dimensional (2D) and three-dimensional (3D) building and tree morphologies on land surface temperature (LST) remain poorly understood. Additionally, there has yet to be a quantitative attempt to separate the direct effects of building and tree morphologies on LST from the indirect effects from 3D perspectives. Here, using Nanjing, China as an example, we investigated the seasonally disparate responses of LST to the 2D and 3D building and tree morphologies, by combining boosted regression trees and a structural equation model (SEM). Our findings show that the predominant factor that affects the spatial pattern of LST is the percent cover of buildings (PER_Build) in spring and summer, while the sky view factor (SVF) dominates LST variations in autumn and winter. The LST responsiveness to changes in these factors also showed a large seasonal variation. In summer, LST increased with PER_Build, and the greatest warming effect occurred when the building surface and tree canopy fractions were lower than 25% and 40%, respectively. In winter, by comparison, the variations in SVF and PER_Tree led to a larger change in LST. Further results using the SEM model suggest that LST variation is governed by more 2D building structures in spring and summer, whereas 3D building structures have a more dominant impact on LST in autumn and winter. Most of the overall impacts resulting from the 3D building and horizontal tree structures are direct throughout each season. However, the overall impacts of the 2D building structure on LST are mainly direct in spring and summer yet indirect in autumn and winter. In all seasons, the indirect impacts of 2D and 3D building structures on LST primarily originate from the horizontal and vertical tree structures, respectively. We consider that these findings can guide designing optimization strategies for urban heat mitigation for different seasons from 2D and 3D perspectives.

Performance-based control co-design of building structures with controlled rocking steel braced frames via neural dynamic model

Controlled rocking steel braced frames (CRSBFs) are modern systems for resilient building structures known for their effective energy dissipation and self-centering features. In a CRSBF, post-tensioned (PT) strands and shear fuses are the distinct features providing the self-centering and energy dissipation mechanisms. As a relatively new structural system, there is a need to study this system’s optimal design. Control co-design has gained interest in recent years as a class of integrated engineering system design methods as an alternative to the traditional approach of optimizing the structural design and sequentially optimizing the control system’s design. It considers the direct relationship between physical and control system design decisions to discover non-obvious design solutions that enable new performance and functionality levels. This paper proposes a performance-based control co-design methodology for building structures integrating CRSBFs that concurrently minimizes the mainframe weight and determines the CRSBF design parameters subject to design code requirements as the optimization constraints. The patented neural dynamic model of Adeli and Park is used in this research to solve the nonlinear optimization problem. The seismic performance evaluation of the proposed methodology includes a shear frame and a nonlinear 6-story 3D building structure. The 6-story building model consists of 282 nodes with 1,692 degrees of freedom. A total of 610 frame elements, 8 PT strands with nonlinear material, and 32 nonlinear links are determined without requiring high-performance computing. Results show that the energy dissipation mechanism effectively reduces the seismic demand in structural members. The proposed performance-based control co-design methodology can lead to 21% reduction in the total weight of the 6-story frame structure.

Separate and combined impacts of building and tree on urban thermal environment from two- and three-dimensional perspectives

Separate impacts of building and tree on the urban thermal environment have been studied extensively, but their combined impacts, especially from both the horizontal (i.e., two-dimensional (2D)) and vertical (i.e., three-dimensional (3D)) perspectives remain largely unclear. Based on satellite thermal data and elaborate 2D and 3D urban morphology, herein we simultaneously investigate the separate and combined impacts of building and tree over Nanjing in China from both the 2D and 3D perspectives. We further examine the day–night contrast together with the sensitivity of such impacts to scale. Our results show that, when compared with urban structures from a single dimension, the combination of 2D and 3D structures is more capable of predicting urban land surface temperatures (LSTs) for both day and night. The assessments further illustrate that the separate and combined impacts of building and tree on LSTs are usually more significant when the spatial scale increases. As for the separate impacts of building and tree, 2D structure affects more urban thermal environment than 3D structure at all spatial scales during the day, but an opposite trend occurs at night. Moreover, for the combined impact of building and tree on LST across different scales, daytime and nighttime LSTs are respectively dominated by 2D and 3D building structures. Combining 2D and 3D structures improves the explained LST variation by 7.3%–11.1% and 25.3%–37.7% for day and night, respectively when compared to using 2D structures only. These findings emphasize the need to incorporate both 2D and 3D urban morphology to improve the urban thermal environment.

Post-earthquake fire behavior and performance-based fire design of steel moment frame buildings

This paper presents an assessment of steel buildings subjected to post-earthquake fire, illustrated through the implementation of a ten-story case study building, which was designed in accordance with U.S. design codes. The building is located in a high seismic region (Los Angeles, CA), and designed for the associated seismic hazard levels using perimeter special moment frames (SMFs) to resist lateral (wind and seismic) loading and gravity interior frames with composite floor systems to resist gravity (dead and live) loading. The entire 3D building structure was idealized and analyzed using finite element models capable of modeling inelastic deformations, instability failures, and connection damage due to earthquakes and fire. The building models were subjected to eleven earthquakes followed by three different fire scenarios to simulate the effects of post-earthquake fires. The results from post-earthquake fire analyses were used to assess the impact of earthquake-induced damage on structural behavior and stability during fire. Assessments indicate that, for the type and design of structure considered in this research, earthquake-induced damage is generally limited to the perimeter moment frames and has limited influence on post-earthquake fire performance, which is governed by the strength and stability of gravity columns and floor systems. Analytical parametric studies were conducted to further assess and develop performance-based design guidelines for improving structural behavior and stability during post-earthquake fires. These studies indicate that partial or full collapse of the building structure can be prevented by sufficiently increasing the structural design (size) or fire protection (fireproofing thickness) of the critical gravity columns.

Mesoscale Modeling of a Masonry Building Subjected to Earthquake Loading

Masonry structures constitute an important part of the built environment and architectural heritage in seismic areas. A large number of these old structures showed inadequate performance and suffered substantial damage under past earthquakes. Realistic numerical models are required for accurate response predictions and for addressing the implementation of effective strengthening solutions. A comprehensive mesoscale modeling strategy explicitly allowing for masonry bond is presented in this paper. It is based on advanced nonlinear material models for interface elements simulating cracks in mortar joints and brick/block units under cyclic loading. Moreover, domain decomposition and mesh tying techniques are used to enhance computational efficiency in detailed nonlinear simulations. The potential of this approach is shown with reference to a case study of a full-scale unreinforced masonry building previously tested in laboratory under pseudodynamic loading. The results obtained confirm that the proposed modeling strategy for brick/block-masonry structures leads to accurate representations of the seismic response of three-dimensional (3D) building structures, both at the local and global levels. The numerical-experimental comparisons show that this detailed modeling approach enables remarkably accurate predictions of the actual dynamic characteristics, along with the main resisting mechanisms and crack patterns.

Automatic Detection of Earthquake-Damaged Buildings by Integrating UAV Oblique Photography and Infrared Thermal Imaging

Extracting damage information of buildings after an earthquake is crucial for emergency rescue and loss assessment. Low-altitude remote sensing by unmanned aerial vehicles (UAVs) for emergency rescue has unique advantages. In this study, we establish a remote sensing information-extraction method that combines ultramicro oblique UAV and infrared thermal imaging technology to automatically detect the structural damage of buildings and cracks in external walls. The method consists of four parts: (1) 3D live-action modeling and building structure analysis based on ultramicro oblique images; (2) extraction of damage information of buildings; (3) detection of cracks in walls based on infrared thermal imaging; and (4) integration of detection systems for information of earthquake-damaged buildings. First, a 3D live-action building model is constructed. A multi-view structure image for segmentation can be obtained based on this method. Second, a method of extracting information on damage to building structures using a 3D live-action building model as the geographic reference is proposed. Damage information of the internal structure of the building can be obtained based on this method. Third, based on analyzing the temperature field distribution on the exterior walls of earthquake-damaged buildings, an automatic method of detecting cracks in the walls by using infrared thermal imaging is proposed. Finally, the damage information detection and assessment system is researched and developed, and the system is integrated. Taking earthquake search-and-rescue simulation as an example, the effectiveness of this method is verified. The damage distribution in the internal structure and external walls of buildings in this area is obtained with an accuracy of 78%.

Visual perception driven 3D building structure representation from airborne laser scanning point cloud

BackgroundThree-dimensional (3D) building models with unambiguous roof plane geometry parameters, roof structure units, and linked topology are essential data for many applications related to human activities in urban environments. The task of 3D reconstruction from point clouds is still in the phase of development, especially to recognize and interpret roof topological structures. MethodsThis paper proposes a novel visual perception-based approach to automatically decompose and reconstruct building point clouds into meaningful and simple parametric structures, while these mutual relationships between roof plane geometry and roof structure units are expressed by a hierarchical topology tree. It starts with roof plane extraction performed by a multi-label graph cut energy optimization framework, and then a roof structure graph (RSG) model is constructed to describe the roof topological geometry with common adjacency, symmetry, and convexity rules. Moreover, a progressive roof decomposition and refinement are performed, generating a hierarchical representation of 3D roof structures models. Finally, a visual plane fitted residuals or areas constraint process is adopted to generate the RSG model in different levels of details. ResultsTwo airborne laser scanning (ALS) datasets with different point densities and roof styles were tested, and the performance evaluation metrics are obtained by the International Society for Photogrammetry and Remote Sensing (ISPRS), achieving correctness and accuracy in terms of 97.7% and 0.29m, respectively. ConclusionsThe standardized assessment results demonstrate the effectiveness and robustness of the proposed approach, showing its abilities to generate a variety of structural models, even in the presence of missing data.

Continental-scale mapping and analysis of 3D building structure

Urban land use is often characterized based on the presence of built-up land, while the land use intensity of different locations is ignored. This narrow focus is at least partially due to a lack of data on the vertical dimension of urban land. The potential of Earth observation data to fill this gap has already been shown, but this has not yet been applied at large spatial scales. This study aims to map urban 3D building structure, i.e. building footprint, height, and volume, for Europe, the US, and China using random forest models. Our models perform well, as indicated by R2 values of 0.90 for building footprint, 0.81 for building height, and 0.88 for building volume, for all three case regions combined. In our multidimensional input variables, we find that built-up density derived from the Global Urban Footprint (GUF) is the most important variable for estimating building footprint, while backscatter intensity of Synthetic Aperture Radar (SAR) is the most important variable for estimating building height. A combination of the two is essential to estimate building volume. Our analysis further highlights the heterogeneity of 3D building structure across space. Specifically, buildings in China tend to be taller on average (10.35 m) compared to Europe (7.37 m) and the US (6.69 m). At the same time, the building volume per capita in China is lowest, with 302.3 m3 per capita, while Europe and the US show estimates of 404.6 m3 and 565.4 m3, respectively. The results of this study (3D building structure data for Europe, the US, and China) are publicly available, and can be used for further analysis of urban environment, spatial planning, and land use projections.

Model-Free Identification of Hysteretic Restoring-Force Characteristic of Multi-Plane and Multi-Story Frame Model With In-Plane Flexible Floor

While linear system identification (SI) has been developed extensively, research advancement in the field of nonlinear hysteretic SI is not satisfactory. An innovative method of identification of hysteretic restoring-force characteristics is proposed in this paper for three-dimensional (3D) building structures with in-plane flexible floors. The hysteretic restoring-force characteristics of vertical structural frames in the 3D building structure are identified from the measured floor horizontal accelerations together with the hysteretic restoring-force characteristics of floors without the assumption of hysteresis types or a priori knowledge of restoring force characteristics. Fourier expansion is applied to the time-history responses of story shear forces in vertical frames and in-plane shear forces in horizontal frames (floors). It is shown that a batch processing least-squares estimation method for many discrete time-domain measured data enables the direct identification of the Fourier coefficients on the story shear forces in vertical structural frames and those on the in-plane shear forces in floors. The proposed method is simple and direct because only Fourier expansion and batch processing least-squares estimation are required. Numerical simulations using measured data without noise and measured data with noise are conducted for investigating the accuracy and reliability of the proposed method. It is demonstrated that the proposed method can identify successfully the hysteretic restoring-force characteristics of plane frames in 3D building structures with in-plane flexible floors.

Output-only damage diagnosis for plan-symmetric buildings with asymmetric damage using modal flexibility-based deflections

Effective methods for vibration-based damage diagnosis in building structures are based on the estimation of structural deflections from identified modal flexibility (MF) matrices. However, most of the existing methods and previous studies that use MF-based deflections were developed by considering the case of buildings that can be modeled as planar structures (e.g. plan-symmetric buildings) both in the baseline and possibly damaged states. In an attempt to fill this research gap, a deflection-based method for output-only damage diagnosis in building structures tested under ambient vibrations is proposed in this paper. The method is applicable for simple rectangular plan-symmetric “box type” 3D building structures which may experience asymmetric damage, and it can be used for detecting, localizing and quantifying the damage. One of the main differences between the proposed and the existing methods is related to the inspection loads adopted to estimate the MF-based deflections. The inspection loads of the proposed method are more complex than the uniform translational loads adopted in the existing methods. Such proposed loads in fact can be assembled only after having estimated the position of the center of stiffness for each story of the structure. According to the proposed approach, the locations of the centers of stiffness are extracted from the experimentally-derived modal flexibility matrices, before performing the damage diagnosis process. The effectiveness and validity of the proposed method was demonstrated both through numerical analyses and by executing experimental ambient vibration tests on a frame building. The results of the proposed method were also compared with the existing approach which was developed for planar structures. This comparison (made both through numerical and experimental analyses) demonstrated that only the proposed approach can be used to perform a correct damage diagnosis process in plan-symmetric buildings which may experience asymmetric damage, especially when considering the damage quantification problem.

Stiffness and Damping Identification for Asymmetric Building Frame With In-plane Flexible Floors

In most building structures, floors with sufficient in-plane stiffness exist and an assumption of rigid in-plane stiffness is valid. However, in some building structures, an assumption of rigid in-plane stiffness does not hold. A method of system identification (SI) for physical parameters (stiffness, damping) is proposed for three-dimensional (3D) building structures with in-plane flexible floors. The stiffness and damping parameters of each vertical structural frame in the 3D building structure are identified from the measured floor horizontal accelerations together with the stiffness and damping parameters of each floor. It is shown that a batch processing least-squares estimation method for many discrete time-domain measured data enables the direct identification of both the stiffness and damping parameters of each vertical structural frame and the stiffness and damping parameters of each floor. The proposed method possesses an advantage that all stiffness and damping parameters of vertical frames and horizontal frames (floors) can be identified simultaneously without search iteration. The accuracy and reliability of the proposed method are made clear by numerical simulations for measured data without noise and measured data with noise. A method of noise elimination is proposed to enhance the identification accuracy. Finally, experiments using a shaking table are conducted for the accuracy investigation of the proposed identification method. It is confirmed that the proposed identification method possesses a reliable ability to identify the stiffness and damping parameters for 3D building structures with in-plane flexible floors.

Prediction of the earthquake response of a three-storey infilled RC structure

Over the last years, the seismic behaviour of infilled reinforced concrete (RC) structures has been the focus of innumerable experimental and numerical studies. A big effort is being made by the scientific community to increase the knowledge regarding the seismic performance of these types of structures and to improve the numerical modelling capabilities to predict their expected behaviour accurately. In 2015, a blind prediction contest was organized by the Faculty of Civil Engineering, University of Osijek, with the main goal of inviting the technical and scientific community to predict the nonlinear seismic behaviour of a 1:2.5 scaled 3D building structure. A three-storey infilled RC structure was subjected to a sequence of ten incremental earthquakes in a shake table test. The blind numerical simulations were carried out by different international teams invited by the organizational committee. The teams knew only the specimen geometry, reinforcement detailing, material properties, and ground motions recorded during the testing. In this context and, in the light of the authors’ success in the contest, the present paper mainly aims at presenting several issues regarding the numerical modelling strategies adopted (and related difficulties) which proved to yield good results while also providing some insight regarding key problems in numerical simulations of infilled RC structures under seismic actions. The main numerical results obtained by all of the participating teams will be presented and compared with the experimental ones. Discussion concerning the reasons behind the mismatching of some results will be presented. Finally, a parametric study was carried out with the aim of assessing the influence of some modelling issues and parameters on the structural global response, in particular when affected by some variations.

Direct Linear System Identification Method for Multistory Three-dimensional Building Structure with General Eccentricity

A method of physical-parameter system identification is proposed here for three-dimensional (3D) building structures with in-plane rigid floors in which the stiffness and damping coefficients of each structural frame in the 3D building structure are identified from the measured floor horizontal accelerations. A batch processing least-squares estimation method for many discrete time-domain measured data is proposed for the direct identification of the stiffness and damping coefficients of each structural frame. While previous researches on the system identification of 3D building structures are limited to a class of structures with regular eccentricity, the present paper removes this limitation. Advantageous features of the proposed identification method are that it is unnecessary to specify the stiffness eccentricities (location of center of stiffness) before identification and the identification of all stiffness and damping parameters can be performed simultaneously. The reliability and accuracy of the proposed method are demonstrated by numerical simulations.

Temporal city modeling using street level imagery

Estimation of the temporal changes to a city is useful for city management, disaster recovery operations, and understanding natural phenomena. When several types of data are available for this task, the optimal type should be chosen depending on the changes that need to be detected. However, data of the desired type are not always available, particularly historical data. In this study, we propose two methods for detecting changes in a city, which can be used in complement to process available data types and detect changes in selected targets. The first method estimates the presence of buildings by comparing street-level images and a 2D city map of buildings created at different points in time. This method uses the Structure from Motion (SfM) technique to reconstruct a point cloud of the structures of the city, and matches the point cloud with the 3D building structures recovered from its 2D map. While 2D city maps are available for most cities, most are not very accurate. Therefore, this method is designed to overcome these inaccuracies and thus is widely applicable. On the other hand, the method cannot detect the following types of scene change: wall paintings, buildings that were reconstructed and closely restored to their previous shape, pedestrians, cars, and vegetation. The second method uses a pair of street-level images that are roughly aligned with GPS data collected at different points in time to detect such scene changes. This method uses the features of a convolutional neural network (CNN) in combination with superpixel segmentation to address inaccurate image alignment and it also enables change detection with pixel-level accuracy. Additionally, the second method is scalable for large-scale estimation because it can quickly detect scene changes by merely using an image pair without performing large-scale SfM. The authors consider the proper use of these two methods to enable temporal city modeling in various situations. We experimentally apply these methods to cities damaged by the tsunami that struck Japan in 2011 and the results show their effectiveness.

Automatic Building Detection based on Supervised Classification using High Resolution Google Earth Images

Abstract. This paper presents a novel approach to detect the buildings by automization of the training area collecting stage for supervised classification. The method based on the fact that a 3d building structure should cast a shadow under suitable imaging conditions. Therefore, the methodology begins with the detection and masking out the shadow areas using luminance component of the LAB color space, which indicates the lightness of the image, and a novel double thresholding technique. Further, the training areas for supervised classification are selected by automatically determining a buffer zone on each building whose shadow is detected by using the shadow shape and the sun illumination direction. Thereafter, by calculating the statistic values of each buffer zone which is collected from the building areas the Improved Parallelepiped Supervised Classification is executed to detect the buildings. Standard deviation thresholding applied to the Parallelepiped classification method to improve its accuracy. Finally, simple morphological operations conducted for releasing the noises and increasing the accuracy of the results. The experiments were performed on set of high resolution Google Earth images. The performance of the proposed approach was assessed by comparing the results of the proposed approach with the reference data by using well-known quality measurements (Precision, Recall and F1-score) to evaluate the pixel-based and object-based performances of the proposed approach. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.4 % and 853 % overall pixel-based and object-based precision performances, respectively.

Risk assessment of steel and steel-concrete composite 3D buildings considering sources of uncertainty

A risk assessment framework for evaluating building structures is implemented in this study. This framework allows considering sources of uncertainty both on structural capacity and seismic demand. In particular randomness on seismic load, incident angle, material properties, floor mass and structural damping are considered; in addition the choice of fibre modelling versus plastic hinge model is also considered as a source of uncertainty. The main objective of this work is to study the contribution of these sources of uncertainty on the fragilities of steel and steel-reinforced concrete composite 3D building structures. The fragility curves are expressed in the form of a two-parameter lognormal distribution where vertical statistics in conjunction with metaheuristic optimization are implemented for calculating the two parameters.