Search Results (418)

Continuous spalling exposure can weaken the performance of structures. Therefore, the development of methods for detecting wall spall damage remains essential in the field of Structural Health Monitoring. Currently, researchers mainly rely on 2D information for spall detection and predominantly use manual data collection methods in the complex environment of residential buildings, which are usually inefficient. To address this challenge, an automated 3D image processing system for wall spalls is proposed in this study. First, UGV path planning was performed in order to collect information about the surrounding environmental defects. Second, to address the shortcomings of RandLA-Net, a dynamic enhanced dual-branch structure is established based on which consistency constraints are introduced, a lightweight attention module is added, and the loss function is optimized in order to enhance the ability of the model in extracting feature information of the point cloud. Finally, spalls are quantitatively evaluated to determine the damage to buildings. The results show that the Randla-Spall achieves 94.71% Recall and 84.20% mIoU on the test set, improved by 4.25% and 5.37%. An integrated process using a lightweight device is achieved in this study, which is capable of efficiently extracting and quantifying spalling defects and provides valuable references for SHM. Full article

Given the significant damage caused by earthquakes over the years, accurate prediction and assessment of the extent of structural damage is critical to ensure safety and guide post-disaster recovery efforts. This study examines the effectiveness and reliability of various damage indexes for reinforced concrete buildings, particularly in the context of seismic events. It highlights the potential of these indexes for future use in digital twin applications or for direct analysis of sensor data recorded during an earthquake, with the ultimate goal of improving real-time damage assessment and decision making. A comprehensive literature review was carried out looking at the damage indexes developed over the last decades. These indexes were applied to a case study involving an RC building with three different structural configurations: a pre-code moment-resisting frame, a code-compliant moment-resisting frame, and a code-compliant shear wall system, both bare and infilled with masonry. The seismic performance of these configurations was evaluated using Multi-Stripe Analyses (MSA) to account for the variability of the seismic input. The results of applying the damage indexes highlight the versatility of these indexes in detecting damage, although some limitations were noted, particularly with cycle-related indicators and their application to infilled structures. The study emphasizes the importance of refining these tools to improve their accuracy and reliability in different structural contexts, ultimately contributing to more accurate seismic damage assessment and damage prediction for specific seismic scenarios. Full article

The advent of digital transformation has redefined the preservation of cultural heritage and historic sites through the integration of Digital Twin technology. Initially developed for industrial applications, Digital Twins are now increasingly employed in heritage conservation as dynamic, digital replicas of physical assets and environments. These systems enable detailed, interactive approaches to documentation, management, and preservation. This paper presents a detailed framework for implementing Digital Twin technology in the management of heritage buildings. By utilizing advanced methods for data collection, processing, and analysis, the framework creates a robust data hub for Digital Twin Heritage Buildings (DT_HB). This architecture enhances real-time monitoring, improves accuracy, reduces operational costs, and enables predictive maintenance while minimizing invasive inspections. Focusing on Bab Al-Mansour Gate in Meknes, Morocco, a significant cultural landmark, this research outlines the workflow for developing a Bab Al-Mansour DT_HB platform. The platform monitors structural health and detects damage over time, offering a dynamic tool for conservation planning. By integrating innovative technologies with data-driven solutions, this study provides a replicable model for preserving heritage sites, addressing critical gaps in real-time monitoring, resource optimization, and environmental risk mitigation. Full article

Floods are considered to be among the most dangerous and destructive geohazards, leading to human victims and severe economic outcomes. Yearly, many regions around the world suffer from devasting floods. The estimation of flood aftermaths is one of the high priorities for the global community. One such flood took place in northern Libya in September 2023. The presented study is aimed at evaluating the flood aftermath for Derna city, Libya, using high resolution GEOEYE-1 and Sentinel-2 satellite imagery in Google Earth Engine environment. The primary task is obtaining and analyzing data that provide high accuracy and detail for the study region. The main objective of study is to explore the capabilities of different algorithms and remote sensing datasets for quantitative change estimation after the flood. Different supervised classification methods were examined, including random forest, support vector machine, naïve-Bayes, and classification and regression tree (CART). The various sets of hyperparameters for classification were considered. The high-resolution GEOEYE-1 images were used for precise change detection using image differencing (pixel-to-pixel comparison and geographic object-based image analysis (GEOBIA) for extracting building), whereas Sentinel-2 data were employed for the classification and further change detection by classified images. Object based image analysis (OBIA) was also performed for the extraction of building footprints using very high resolution GEOEYE images for the quantification of buildings that collapsed due to the flood. The first stage of the study was the development of a workflow for data analysis. This workflow includes three parallel processes of data analysis. High-resolution GEOEYE-1 images of Derna city were investigated for change detection algorithms. In addition, different indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed NDVI (TNDVI), and normalized difference moisture index (NDMI)) were calculated to facilitate the recognition of damaged regions. In the final stage, the analysis results were fused to obtain the damage estimation for the studied region. As the main output, the area changes for the primary classes and the maps that portray these changes were obtained. The recommendations for data usage and further processing in Google Earth Engine were developed. Full article

This study presents the results of an experimental investigation conducted on a 2:3 scale model of a two-story stone masonry building. We tested the model on the UniKORE L.E.D.A. lab shake table, simulating the Mw 6.3 earthquake ground motion that struck L’Aquila, Italy, on 6 April 2009, with progressively increasing peak acceleration levels. We installed a network of accelerometric sensors on the model to capture its structural behaviour under seismic excitation. Medium-to lower-cost MEMS accelerometers (classes A and B) were compared with traditional piezoelectric sensors commonly used in Structural Health Monitoring (SHM). The experiment assessed the structural performance and damage progression of masonry buildings subjected to realistic earthquake inputs. Additionally, the collected data provided valuable insights into the effectiveness of different sensor types and configurations in detecting key vibrational and failure patterns. All the sensors were able to accurately measure the dynamic response during seismic excitation. However, not all of them were suitable for Operational Modal Analysis (OMA) in noisy environments, where their self-noise represents a crucial factor. This suggests that the self-noise of MEMS accelerometers must be less than 1 µg/√Hz, or preferably below 0.5 µg/√Hz, to obtain good results from the OMA. Therefore, we recommend ultra-low-noise sensors for detecting differences in the structural behaviour before and after seismic events. Our findings provide valuable insights into the seismic vulnerability of masonry structures and the effectiveness of sensors in detecting damage. The management of buildings in earthquake-prone areas can benefit from these specifications. Full article

The environment continues to erode the roofs of ancient buildings in Longmen Ancient Town, posing a threat to the safety of villagers. Scientific detection and diagnosis are important steps in the repair and protection of historical buildings. In order to effectively protect cultural heritage, this study uses the YOLOv8 deep learning model to automatically detect damage on images of traditional residential roofs. The researchers constructed image data sets for the four categories of green vegetation, dry vegetation, missing tiles, and repaired tiles and then perform model training. The results show that the model is generally accurate for missing tiles (0.94 for missing tiles and 0.93 for repaired tiles), and it has a low false detection rate and a low missed detection rate. It does make some mistakes when it comes to green and dry vegetation in complex backgrounds, but the overall detection coverage and F1 score are better. This practical application shows that the model can accurately mark most target areas, especially for the recognition of high-contrast damage types. This study provides efficient and accurate technical support for the diagnosis of traditional roof structures and protection of cultural heritage. Full article

The biodeterioration process involves the alteration of stone monuments by living organisms, such as bacteria, algae, fungi, lichens, mosses, ferns, and vascular plants, combined with abiotic factors, resulting in physical and chemical damage to historic buildings. This study aims to investigate the role of the vascular plants affecting four archaeological parks in Campania—Pompeii, Herculaneum, Paestum, and Velia—by analyzing correlations with building materials, exposure, and conservation status. To represent species associations and their coverage percentages at each site, transects of one square meter were employed. The hazard index (HI) was applied to evaluate the impact of the identified biodeteriogens. A total of 117 species were detected across 198 samples collected from the four study sites, with 59 taxa recorded in Pompeii, 56 in Paestum, 41 in Velia, and 36 in Herculaneum. Specifically, Pompeii hosts a predominance of cosmopolitan species (35%) and widely distributed taxa (15%) due to elevated anthropogenic disturbance. Conversely, mediterranean species dominate in Paestum (62%) and Herculaneum (52%), reflecting more stable ecological conditions. Substrate type significantly influences the hazard index, whereas exposure was found to have minimal impact on both the average coverage and the measured hazard index. Future work will focus on developing site-specific conservation strategies that consider substrate properties, vegetation impact, and anthropogenic disturbances to effectively mitigate the biodeterioration risks posed by vascular flora in Italian monumental sites. Full article

Due to the high-density coverage of vegetation, the complexity of terrain, and occlusion issues, ground point extraction faces significant challenges. Airborne Light Detection and Ranging (LiDAR) technology plays a crucial role in complex mountainous areas. This article proposes a method for constructing fine terrain in high vegetation coverage areas based on implicit neural representation. This method consists of data preprocessing, multi-scale and multi-feature high-difference point cloud initial filtering, and an upsampling module based on implicit neural representation. Firstly, preprocess the regional point cloud data is preprocessed; then, K-dimensional trees (K-d trees) are used to construct spatial indexes, and spherical neighborhood methods are applied to capture the geometric and physical information of point clouds for multi-feature fusion, enhancing the distinction between terrain and non-terrain elements. Subsequently, a differential model is constructed based on DSM (Digital Surface Model) at different scales, and the elevation variation coefficient is calculated to determine the threshold for extracting the initial set of ground points. Finally, the upsampling module using implicit neural representation is used to finely process the initial ground point set, providing a complete and uniformly dense ground point set for the subsequent construction of fine terrain. To validate the performance of the proposed method, three sets of point cloud data from mountainous terrain with different features are selected as the experimental area. The experimental results indicate that, from a qualitative perspective, the proposed method significantly improves the classification of vegetation, buildings, and roads, with clear boundaries between different types of terrain. From a quantitative perspective, the Type I errors of the three selected regions are 4.3445%, 5.0623%, and 5.9436%, respectively. The Type II errors are 5.7827%, 6.8516%, and 7.3478%, respectively. The overall errors are 5.3361%, 6.4882%, and 6.7168%, respectively. The Kappa coefficients of the measurement areas all exceed 80%, indicating that the proposed method performs well in complex mountainous environments. Provide point cloud data support for the construction of wind and photovoltaic bases in China, reduce potential damage to the ecological environment caused by construction activities, and contribute to the sustainable development of ecology and energy. Full article

Modern high-intensity armed conflicts often lead to extensive damage to urban infrastructure. The use of high-resolution remote sensing images can clearly detect damage to individual buildings which is of great significance for monitoring war crimes and damage assessments that destroy civilian infrastructure indiscriminately. In this paper, we propose SOCA-YOLO (Sampling Optimization and Coordinate Attention–YOLO), an automatic detection method for destroyed buildings in high-resolution remote sensing images based on deep learning techniques. First, based on YOLOv8, Haar wavelet transform and convolutional blocks are used to downsample shallow feature maps to make full use of spatial details in high-resolution remote sensing images. Second, the coordinate attention mechanism is integrated with C2f so that the network can use the spatial information to enhance the feature representation earlier. Finally, in the feature fusion stage, a lightweight dynamic upsampling strategy is used to improve the difference in the spatial boundaries of feature maps. In addition, this paper obtained high-resolution remote sensing images of urban battlefields through Google Earth, constructed a dataset for the detection of objects on buildings, and conducted training and verification. The experimental results show that the proposed method can effectively improve the detection accuracy of destroyed buildings, and the method is used to map destroyed buildings in cities such as Mariupol and Volnovaja where violent armed conflicts have occurred. The results show that deep learning-based object detection technology has the advantage of fast and accurate detection of destroyed buildings caused by armed conflict, which can provide preliminary reference information for monitoring war crimes and assessing war losses. Full article

Unmanned aerial vehicle (UAV) vision-based sensing has become an emerging technology for structural health monitoring (SHM) and post-disaster damage assessment of civil infrastructure. This article proposes a framework for monitoring structural displacement under earthquakes by reprojecting image points obtained courtesy of UAV-captured videos to the 3-D world space based on the world-to-image point correspondences. To identify optimal features in the UAV imagery, geo-reference targets with various patterns were installed on a test building specimen, which was then subjected to earthquake shaking. A feature point tracking-based algorithm for square checkerboard patterns and a Hough Transform-based algorithm for concentric circular patterns are developed to ensure reliable detection and tracking of image features. Photogrammetry techniques are applied to reconstruct the 3-D world points and extract structural displacements. The proposed methodology is validated by monitoring the displacements of a full-scale 6-story mass timber building during a series of shake table tests. Reasonable accuracy is achieved in that the overall root-mean-square errors of the tracking results are at the millimeter level compared to ground truth measurements from analog sensors. Insights on optimal features for monitoring structural dynamic response are discussed based on statistical analysis of the error characteristics for the various reference target patterns used to track the structural displacements. Full article

Sewer infrastructure is vital for flood prevention, environmental protection, and public health. As part of sewer infrastructure, sewer systems are prone to degradation. Traditional maintenance methods for sewer systems are largely manual and reactive and rely on inconsistent data, leading to inefficient maintenance. The KaSyTwin research project addresses the urgent need for efficient and resilient sewer system management methods in Germany, aiming to develop a methodology for the semi-automated development and utilization of digital twins of sewer systems to enhance data availability and operational resilience. Using advanced multi-sensor robotic platforms equipped with scanning and imaging systems, i.e., laser scanners and cameras, as well as artificial intelligence (AI), the KaSyTwin research project focuses on generating digital twin-enabled representations of sewer systems in real time. As a project report, this work outlines the research framework and proposed methodologies in the KaSyTwin research project. Digital twins of sewer systems integrated with AI technologies are expected to facilitate proactive maintenance, resilience forecasting against extreme weather events, and real-time damage detection. Furthermore, the KaSyTwin research project aspires to advance the digital management of sewer systems, ensuring long-term functionality and public welfare via on-demand structural health monitoring and non-destructive testing. Full article

Due to the uncertainty of material properties of plate-like structures, many traditional methods are unable to locate the impact source on their surface in real time. It is important to study the impact source-localization problem for plate structures. In this paper, a data-driven machine learning method is proposed to detect impact sources in plate-like structures and its effectiveness is tested on three plate-like structures with different material properties. In order to collect data on the localization of the impact source, four piezoelectric transducers and an oscilloscope were utilized to construct an experimental platform for impulse response testing. Meanwhile, the position of the impact source on the surface of the test plate is generated by manually releasing the steel ball. The eigenvalue of arrival time in the time domain signal is extracted to build data sets for machine learning. This paper uses the Back Propagation (BP) neural network to learn the difference in the arrival time of each sensor and predict the location of the impact source. The results demonstrate that the machine learning method proposed in this paper can predict the location of the impact source in the plate-like structure without relying on the material properties, with high test accuracy and robustness. The research work in this paper can provide experimental methods and testing techniques for locating impact damage in composite material structures. Full article

The widely used Bouc–Wen–Baber–Noori (BWBN) hysteresis model, although effective in simulating hysteresis behaviors, does not account for variations in the pinching region of hysteretic behaviors. This can negatively impact the accuracy of the BWBN model in simulating structural responses and damage mechanisms in structures such as reinforced concrete (RC) and timber, which exhibit highly pinched hysteresis behavior when damaged by earthquakes. This paper introduces a BWBN model with varying pinching region characteristics (BWBN-VP model) which can degrade pinching stiffness and increase pinching effects under seismic loads. Unlike the original BWBN model using constant pinching stiffness (kp), this modified new model, inspired by real-world structural damage, improves structural damage detection, identifiability, and analysis in real-world scenarios. Model validation uses experimental data from three RC column tests with different failure modes and hysteresis loop shapes, resulting in an ~0.98 correlation coefficient between the experimental and simulated responses. Further validation uses real-world seismic data from a six-story RC building and achieves an average correlation of ~0.97 with a minor 2.5% difference in the peak restoring forces compared to direct measurements. The proposed BWBN-VP model also accurately and realistically captures damage to both the elastic and pinching stiffness values of the building, with an average difference of ~4%. Results confirm that the BWBN-VP model, compared to the original, more accurately predicts hysteretic responses, especially in Shear Failure (SF) modes. Therefore, the BWBN-VP model, superior in simulating highly pinched behaviors in RC and timber structures, would be an advanced tool for resilient seismic design and Structural Health Monitoring (SHM). Full article

Given that numerous countries are located near active fault zones, this review paper assesses the seismic structural functionality of buildings subjected to dynamic loads. Earthquake-prone countries have implemented structural health monitoring (SHM) systems on base-isolated structures, focusing on modal parameters such as frequencies, mode shapes, and damping ratios related to isolation systems. However, many studies have investigated the dissipating energy capacity of isolation systems, particularly rubber bearings with different damping ratios, and demonstrated that changes in these parameters affect the seismic performance of structures. The main objective of this review is to evaluate the performance of damage detection computational tools and examine the impact of damage on structural functionality. This literature review’s strength lies in its comprehensive coverage of prominent studies on SHM and model updating for structures equipped with dampers. This is crucial for enhancing the safety and resilience of structures, particularly in mitigating dynamic loads like seismic forces. By consolidating key research findings, this review identifies technological advancements, best practices, and gaps in knowledge, enabling future innovation in structural health monitoring and design optimization. Various identification techniques, including modal analysis, model updating, non-destructive testing (NDT), and SHM, have been employed to extract modal parameters. The review highlights the most operational methods, such as Frequency Domain Decomposition (FDD) and Stochastic Subspace Identification (SSI). The review also summarizes damage identification methodologies for base-isolated systems, providing useful insights into the development of robust, trustworthy, and effective techniques for both researchers and engineers. Additionally, the review highlights the evolution of SHM and model updating techniques, distinguishing groundbreaking advancements from established methods. This distinction clarifies the trajectory of innovation while addressing the limitations of traditional techniques. Ultimately, the review promotes innovative solutions that enhance accuracy, reliability, and adaptability in modern engineering practices. Full article

To establish a finite element model that accurately represents the dynamic characteristics of actual super high-rise building and improve the accuracy of the finite element simulation results, a finite element model updating method for super high-rise building is proposed based on the response surface method (RSM). Taking a 120 m super high-rise building as the research object, a refined initial finite element model is firstly established, and the elastic modulus and density of the main concrete and steel components in the model are set as the parameters to be updated. A significance analysis was conducted on 16 parameters to be updated including E1–E8, D1–D8, and the first 10 natural frequencies of the structure, and 6 updating parameters are ultimately selected. A sample set of updating parameters was generated using central composite design (CCD) and then applied to the finite element model for calculation. The response surface equations for the first ten natural frequencies were obtained through quadratic polynomial fitting, and the optimal solution of the objective function was determined using a genetic algorithm. The results of the engineering case study indicate that the errors in the first ten natural frequencies of the updated finite element model are all within 5%. The updated model accurately reflects the current situation of the super high-rise building and provides a basis for super high-rise building health monitoring, damage detection, and reliability assessment. Full article