基于三维激光扫描的房屋尺寸质量智能化检测方法

基于三维激光扫描技术,提出了一种智能化、全流程的房屋尺寸质量检测方法,包括点云数据配准、点云数据轻量化、房屋逆向建模及尺寸质量检测。通过点云数据映射全景图、基于YOLO v5神经网络模型的标靶纸目标检测以及基于模板匹配方法的标靶中心估计等步骤,可实现多站点云数据之间的自动配准; 通过可分解图滤波算法进行点云数据重采样,实现数据轻量化并提高运行速度; 针对房屋整体点云数据,提出了集点云数据分割、表面重建、尺寸质量检测于一体的综合算法。结果表明:基于标靶纸的点云配准方法能够自动完成各站点云数据的配准,得到完整房屋点云数据; 点云数据分割技术能够分离不同墙面、楼板底面和顶面的点云数据; 表面重建算法能够生成房屋的实体模型; 尺寸质量检测技术能够自动计算出表面的平整度和垂直度; 提出的房屋尺寸质量检测方法全面、可行,且能够适用不同的户型,研究成果以期替代人工测量完成房屋的平整度与垂直度的检测。     

Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks

A number of image processing techniques (IPTs) have been implemented for detecting civil infrastructure defects to partially replace human‐conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real‐world situations (e.g., lighting and shadow changes) can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision‐based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions (e.g., strong light spot, shadows, and very thin cracks). Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations.     

Automatic creation of semantically rich 3D building models from laser scanner data

In the Architecture, Engineering, and Construction (AEC) domain, semantically rich 3D information models are increasingly used throughout a facility's life cycle for diverse applications, such as planning renovations, space usage planning, and managing building maintenance. These models, which are known as building information models (BIMs), are often constructed using dense, three dimensional (3D) point measurements obtained from laser scanners. Laser scanners can rapidly capture the “as-is” conditions of a facility, which may differ significantly from the design drawings. Currently, the conversion from laser scan data to BIM is primarily a manual operation, and it is labor-intensive and can be error-prone. This paper presents a method to automatically convert the raw 3D point data from a laser scanner positioned at multiple locations throughout a facility into a compact, semantically rich information model. Our algorithm is capable of identifying and modeling the main visible structural components of an indoor environment (walls, floors, ceilings, windows, and doorways) despite the presence of significant clutter and occlusion, which occur frequently in natural indoor environments. Our method begins by extracting planar patches from a voxelized version of the input point cloud. The algorithm learns the unique features of different types of surfaces and the contextual relationships between them and uses this knowledge to automatically label patches as walls, ceilings, or floors. Then, we perform a detailed analysis of the recognized surfaces to locate openings, such as windows and doorways. This process uses visibility reasoning to fuse measurements from different scan locations and to identify occluded regions and holes in the surface. Next, we use a learning algorithm to intelligently estimate the shape of window and doorway openings even when partially occluded. Finally, occluded surface regions are filled in using a 3D inpainting algorithm. We evaluated the method on a large, highly cluttered data set of a building with forty separate rooms.     

Encoder–decoder network for pixel‐level road crack detection in black‐box images

Timely monitoring of pavement cracks is essential for successful maintenance of road infrastructure. Accurate information concerning crack location and severity enables proactive management of the infrastructure. Black‐box cameras, which are becoming increasingly widespread at an affordable price, can be used as efficient road‐image collectors over a wide area. However, the cracks in these images are difficult to detect, because the images containing them often include objects other than roads. Thus, we propose a pixel‐level detection method for identifying road cracks in black‐box images using a deep convolutional encoder–decoder network. The encoder consists of convolutional layers of the residual network for extracting crack features, and the decoder consists of deconvolutional layers for localizing the cracks in an input image. The proposed network was trained on 427 out of 527 images extracted from black‐box videos and tested on the remaining 100 images. Compared with VGG‐16, ResNet‐50, ResNet‐101, ResNet‐200 with transfer learning, and ResNet‐152 without transfer learning, ResNet‐152 with transfer learning exhibited the best performance, achieving recall, precision, and intersection of union of 71.98%, 77.68%, and 59.65%, respectively. The experimental results prove that the proposed method is optimal for detecting cracks in black‐box images at the pixel level.     

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Deep learning has ushered in many breakthroughs in vision‐based detection via convolutional neural networks (CNNs), but the vibration‐based structural damage detection by CNN remains being refined. Thus, this study proposes a simple one‐dimensional CNN that detects tiny local structural stiffness and mass changes, and validates the proposed CNN on actual structures. Three independent acceleration databases are established based on a T‐shaped steel beam, a short steel girder bridge (in test field), and a long steel girder bridge (in service). The raw acceleration data are not pre‐processed and are directly used as the training and validation data. The well‐trained CNN almost perfectly identifies the locations of small local changes in the structural mass and stiffness, demonstrating the high sensitivity of the proposed simple CNN to tiny structural state changes in actual structures. The convolutional kernels and outputs of the convolutional and max pooling layers are visualized and discussed as well.     

A Condition‐Based Deterioration Model for the Stochastic Dependency of Corrosion Rate and Crack Propagation in Corroded Concrete Structures

Physics‐based models are intensively studied in mechanical and civil engineering but their constant increase in complexity makes them harder to use in a maintenance context, especially when degradation model can/should be updated from new inspection data. On the other hand, Markovian cumulative damage approaches such as Gamma processes seem promising; however, they suffer from lack of acceptability by the civil engineering community due to poor physics considerations. In this article, we want to promote an approach for modeling the degradation of structures and infrastructures for maintenance purposes which can be seen as an intermediate approach between physical models and probabilistic models. A new statistical, data‐driven state‐dependent model is proposed. The construction of the degradation model will be discussed within an application to the cracking of concrete due to chloride‐induced corrosion. Numerical experiments will later be conducted to identify preliminary properties of the model in terms of statistical inferences. An estimation algorithm is proposed to estimate the parameters of the model in cases where databases suffer from irregularities.     

Three‐dimensional multiscale finite element models for in‐service performance assessment of bridges

Accurate representation of the structural performance of civil engineering structures, specifically complex bridge structures, may be achieved through an efficient multiscale finite element (FE) model. Multiscale FE modeling couples multiple dimensions of elements in a single model. In this study, the selected existing multipoint constraint equations applied in planar coupling conditions are modified and refined for out‐of‐plane coupling conditions in a single three‐dimensional FE model. Also, the optimum location for the interface points of different elements is determined to improve the model's accuracy and efficiency. The present case study, the Memorial Bridge in Portsmouth, NH, is a vertical lift bridge, which includes novel gusset‐less connections. These connections have complex geometries and therefore require finer dimension elements to represent the structural behavior, while the remainder of the structure is modeled with coarser dimension elements. To achieve an accurate and efficient multiscale model of the Memorial Bridge, multiple global FE models are developed and the predicted structural responses are verified with respect to the field‐collected structural responses of the bridge.     

A graph‐based method for social sensing of infrastructure disruptions in disasters

Damages in critical infrastructure occur abruptly, and disruptions evolve with time dynamically. Understanding the situation of critical infrastructure disruptions is essential to effective disaster response and recovery of communities. Although the potential of social media data for situation awareness during disasters has been investigated in recent studies, the application of social sensing in detecting disruptions and analyzing evolutions of the situation about critical infrastructure is limited. To address this limitation, this study developed a graph‐based method for detecting credible situation information related to infrastructure disruptions in disasters. The proposed method was composed of data filtering, burst time‐frame detection, content similarity calculation, graph analysis, and situation evolution analysis. The application of the proposed method was demonstrated in a case study of Hurricane Harvey in 2017 in Houston. The findings highlighted the capability of the proposed method in detecting credible situational information and capturing the temporal and spatial patterns of critical infrastructure events that occurred in Harvey, including disruptive events and their adverse impacts on communities. The proposed methodology can improve the ability of community members, volunteer responders, and decision makers to detect and respond to infrastructure disruptions in disasters.     

Unified Vision‐Based Methodology for Simultaneous Concrete Defect Detection and Geolocalization

Vision‐based autonomous inspection of concrete surface defects is crucial for efficient maintenance and rehabilitation of infrastructures and has become a research hot spot. However, most existing vision‐based inspection methods mainly focus on detecting one kind of defect in nearly uniform testing background where defects are relatively large and easily recognizable. But in the real‐world scenarios, multiple types of defects often occur simultaneously. And most of them occupy only small fractions of inspection images and are swamped in cluttered background, which easily leads to missed and false detections. In addition, the majority of the previous researches only focus on detecting defects but few of them pay attention to the geolocalization problem, which is indispensable for timely performing repair, protection, or reinforcement works. And most of them rely heavily on GPS for tracking the locations of the defects. However, this method is sometimes unreliable within infrastructures where the GPS signals are easily blocked, which causes a dramatic increase in searching costs. To address these limitations, we present a unified and purely vision‐based method denoted as defects detection and localization network, which can detect and classify various typical types of defects under challenging conditions while simultaneously geolocating the defects without requiring external localization sensors. We design a supervised deep convolutional neural network and propose novel training methods to optimize its performance on specific tasks. Extensive experiments show that the proposed method is effective with a detection accuracy of 80.7% and a localization accuracy of 86% at 0.41 s per image (at a scale of 1,200 pixels in the field test experiment), which is ideal for integration within intelligent autonomous inspection systems to provide support for practical applications.     

Zernike‐moment measurement of thin‐crack width in images enabled by dual‐scale deep learning

Although crack inspection is a routine practice in civil infrastructure management (especially for highway bridge structures), it is time‐consuming and safety‐concerning to trained engineers and costly to the stakeholders. To automate this in the near future, the algorithmic challenge at the onset is to detect and localize cracks in imagery data with complex scenes. The rise of deep learning (DL) sheds light on overcoming this challenge through learning from imagery big data. However, how to exploit DL techniques is yet to be fully explored. One primary component of practical crack inspection is that it is not merely detection via visual recognition. To evaluate the potential risk of structural failure, it entails quantitative characterization, which usually includes crack width measurement. To further facilitate the automation of machine‐vision‐based concrete crack inspection, this article proposes a DL‐enabled quantitative crack width measurement method. In the detection and mapping phase, dual‐scale convolutional neural networks are designed to detect cracks in complex scene images with validated high accuracy. Subsequently, a novel crack width estimation method based on the use of Zernike moment operator is further developed for thin cracks. The experimental results based on a laboratory loading test agree well with the direct measurements, which substantiates the effectiveness of the proposed method for quantitative crack detection.     

Structural Displacement Measurement Using an Unmanned Aerial System

Vibration‐based Structural Health Monitoring (SHM) is one of the most popular solutions to assess the safety of civil infrastructure. SHM applications all begin with measuring the dynamic response of structures, but displacement measurement has been limited by the difficulty in requiring a fixed reference point, high cost, and/or low accuracy. Recently, researchers have conducted studies on vision‐based structural health monitoring, which provides noncontact and efficient measurement. However, these approaches have been limited to stationary cameras, which have the challenge of finding a location to deploy the cameras with appropriate line‐of‐sight, especially to monitor critical civil infrastructures such as bridges. The Unmanned Aerial System (UAS) can potentially overcome the limitation of finding optimal locations to deploy the camera, but existing vision‐based displacement measurement methods rely on the assumption that the camera is stationary. The displacements obtained by such methods will be a relative displacement of a structure to the camera motion, not an absolute displacement. Therefore, this article presents a framework to achieve absolute displacement of a structure from a video taken from an UAS using the following phased approach. First, a target‐free method is implemented to extract the relative structural displacement from the video. Next, the 6 degree‐of‐freedom camera motion (three translations and three rotations) is estimated by tracking the background feature points. Finally, the absolute structural displacement is recovered by combining the relative structural displacement and the camera motion. The performance of the proposed system has been validated in the laboratory using a commercial UAS. Displacement of a pinned‐connected railroad truss bridge in Rockford, IL subjected to revenue‐service traffic loading was reproduced on a hydraulic simulator, while the UAS was flown from a distance of 4.6 m (simulating the track clearance required by the Federal Railroad Administration), resulting in estimated displacements with an RMS error of 2.14 mm.     

Forward‐Backward Approach for 3D Event Localization Using Commodity Smartphones for Ubiquitous Context‐Aware Applications in Civil and Infrastructure Engineering

An inexpensive and robust 3D localization system for tracking the position of a user in GPS‐ or WLAN‐denied environments offers significant potential for improving decision‐making tasks for civil and infrastructure engineering applications. To this end, an infrastructure‐free approach for 3D event localization on commodity smartphones is presented. In the proposed method, the position of the user is continuously tracked based on the smartphone sensory data (the Forward approach) until the user reaches a certain event. Here, an event location refers to the 3D location of a user conducting value‐added activities such as tasks involved in emergency response and field reporting of operational issues. Once an event is observed, the motion trajectory of the user is backtracked from the postevent landmark to reestimate the location of the event (the Backward approach). By integrating probability distributions of the Forward and Backward approaches together, the proposed method derives the most‐likely location of the event. To validate the proposed approach, seven case studies are conducted in a multistory parking garage. The experimental results show that the probabilistic integration of the localization results from the Forward and Backward dead reckonings can produce more accurate 3D localization results when compared to a single best estimate from a one‐way dead reckoning process. Lessons learned from several real‐world case studies and open research challenges in improving localization accuracy are discussed in detail.     

Automated crack evaluation of a high‐rise bridge pier using a ring‐type climbing robot

This article proposes a deep learning‐based automated crack evaluation technique for a high‐rise bridge pier using a ring‐type climbing robot. First, a ring‐type climbing robot system composed of multiple vision cameras, climbing robot, and control computer is developed. By spatially moving the climbing robot system along a target bridge pier with close‐up scanning condition, high‐quality raw vision images are continuously obtained. The raw vision images are then processed through feature control‐based image stitching, deep learning‐based semantic segmentation, and Euclidean distance transform–based crack quantification algorithms. Finally, a digital crack map on the region of interest (ROI) of the target bridge pier can be automatically established. The proposed technique is experimentally validated using in situ test data obtained from Jang–Duck bridge in South Korea. The test results reveal that the proposed technique successfully evaluates cracks on the entire ROI of the bridge pier with precision of 90.92% and recall of 97.47%.     

Simplified analysis of asymmetric high‐rise structures with cores

A simplified elastic hand‐method of analysis for asymmetric multi‐bent structures with cores subjected to horizontal loading is presented. The structures may consist of combinations of framed structures such as coupled walls, rigid frames and braced frames with planar and non‐planar shear walls. Results for structures that are uniform with height compare closely with results from stiffness matrix analyses. The method is developed from coupled‐wall deflection theory which is expressed in non‐dimensional structural parameters. It accounts for bending deformations in all individual members, axial deformations in the vertical members as well as torsion and warping in nonplanar walls. A closed solution of coupled differential equations for deflection and rotation gives the deflected shape along the height of the building from which all internal forces can be obtained. The proposed method of analysis offers a relatively simple and rapid means of comparing the deformations and internal forces of different stability systems for a proposed tall building in the preliminary stages of the design. The derivation of equations for analysis shown in this paper are for unisymmetric stability systems only, but the method is also applicable to general asymmetric structures with cores. Copyright © 2002 John Wiley & Sons, Ltd.     

Bauwerksüberwachung der Autobahnbrücke Gossensaß

Structural Monitoring of Concrete Bridges The condition assessment of aged structures is becoming a more and more important issue for civil infrastructure management systems. The continued use of existing systems is, due to environmental, economical and socio‐political assets, of great significance growing larger every year. In front of this background we find the necessity to identify the key parameters and procedures to verify and update the knowledge about the present condition of a structure. A visual inspection may yield a first qualitative, maybe purely intuitive impression. A better judgment would be based on the evaluation of acquired quantitative information. The present contribution is intended to describe the implementation as well as an excerpt of the fundamental results of the online‐monitoring‐system realized on the Gossensaß viaduct, addressing durability and serviceability aspects.     

Sensor Attitude Correction of Wireless Sensor Network for Acceleration‐Based Monitoring of Civil Structures

Wireless sensors are now becoming practical alternatives to traditional wired sensors in monitoring civil structures. Though many have been reported on acceleration‐based monitoring of civil structures using wireless sensor networks, sensor attitude that may be different from instrumentation plan has been a seemingly overlooked issue behind performance validation of the network. In this article, a technique to correct the sensor attitude is proposed for the wireless sensor network that measures 3D acceleration of civil structures. Six simple formulas to assess the well‐known 3D Euler angles (i.e., roll, pitch, and yaw) are derived using the gravity extracted from measured 3D acceleration and nonchanging direction of sensors on a stationary structure. The proposed technique is validated at a large‐scale wireless sensor network with 22 sensors in the respective attitudes on a truss bridge. First, attitudes assessed by the proposed method are compared with instrumentation plan. Then, mode shapes obtained before and after the correction are compared with those from finite element model. Comparison shows that quality of the mode shapes improves significantly by small amount of attitude correction less than 7°.     

Life-cycle performance,management, and optimisation of structural systems under uncertainty: accomplishments and challenges 1

Our knowledge to model, analyse, design, maintain, monitor, manage, predict and optimise the life-cycle performance of structures and infrastructures under uncertainty is continually growing. However, in many countries, including the United States, the civil infrastructure is no longer within desired levels of performance and safety. Decisions regarding civil infrastructure systems should be supported by an integrated reliability-based life-cycle multi-objective optimisation framework by considering, among other factors, the likelihood of successful performance and the total expected cost accrued over the entire life-cycle. The primary objective of this paper is to highlight recent accomplishments in the life-cycle performance assessment, maintenance, monitoring, management and optimisation of structural systems under uncertainty. Challenges are also identified.     

Wavelet‐Based Denoising for Traffic Volume Time Series Forecasting with Self‐Organizing Neural Networks

Abstract: In their goal to effectively manage the use of existing infrastructures, intelligent transportation systems require precise forecasting of near‐term traffic volumes to feed real‐time analytical models and traffic surveillance tools that alert of network links reaching their capacity. This article proposes a new methodological approach for short‐term predictions of time series of volume data at isolated cross sections. The originality in the computational modeling stems from the fit of threshold values used in the stationary wavelet‐based denoising process applied on the time series, and from the determination of patterns that characterize the evolution of its samples over a fixed prediction horizon. A self‐organizing fuzzy neural network is optimized in its configuration parameters for learning and recognition of these patterns. Four real‐world data sets from three interstate roads are considered for evaluating the performance of the proposed model. A quantitative comparison made with the results obtained by four other relevant prediction models shows a favorable outcome.     

Concrete crack detection using context‐aware deep semantic segmentation network

Computer‐vision and deep‐learning techniques are being increasingly applied to inspect, monitor, and assess infrastructure conditions including detection of cracks. Traditional vision‐based methods to detect cracks lack accuracy and generalization to work on complicated infrastructural conditions. This paper presents a novel context‐aware deep convolutional semantic segmentation network to effectively detect cracks in structural infrastructure under various conditions. The proposed method applies a pixel‐wise deep semantic segmentation network to segment the cracks on images with arbitrary sizes without retraining the prediction network. Meanwhile, a context‐aware fusion algorithm that leverages local cross‐state and cross‐space constraints is proposed to fuse the predictions of image patches. This method is evaluated on three datasets: CrackForest Dataset (CFD) and Tomorrows Road Infrastructure Monitoring, Management Dataset (TRIMMD) and a Customized Field Test Dataset (CFTD) and achieves Boundary F1 (BF) score of 0.8234, 0.8252, and 0.7937 under 2‐pixel error tolerance margin in CFD, TRIMMD, and CFTD, respectively. The proposed method advances the state‐of‐the‐art performance of BF score by approximately 2.71% in CFD, 1.47% in TRIMMD, and 4.14% in CFTD. Moreover, the averaged processing time of the proposed system is 0.7 s on a typical desktop with Intel^® Quad‐Core? i7‐7700 CPU@3.6 GHz Processor, 16GB RAM and NVIDIA GeForce GTX 1060 6GB GPU for an image of size 256 × 256 pixels.