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Abstract Over-height vehicle strikes with low bridges and tunnels are an ongoing problem worldwide. While previous methods have used vision-based systems to address the over-height warning problem, such methods are sensitive to wind. In this paper, we perform a full validation of the system using a constraint-based approach to minimize the number of over-height vehicle misclassifications due to windy conditions. The dataset includes a total of 102 over-height vehicles recorded at frame rates of 25 and 30fps. An analysis is performed of wind and vehicle displacements to track over-height features using optical flow paired with SURF feature detectors. Motion captured within the region of interest was treated as a standard two-class binary linear classification problem with 1 indicating over-height vehicle presence and 0 indicating noise. The algorithm performed with 100% recall, 83.3% precision, false positive rate of 0.2% and warning accuracy of 96.6%. [ABSTRACT FROM AUTHOR]

Teams of engineers visually inspect more than half a million bridges per year in the US and EU. There is clear evidence to suggest that they are not able to meet all bridge inspection guideline requirements due to a combination of the level of detail expected, the limited time available and the large area of bridge surfaces to be inspected. Methods have been proposed to address this problem through damage detection in visual data, yet the inspection load remains high. This paper proposes a method to tackle this problem by detecting (and disregarding) healthy concrete areas that comprise over 80–90% of the total area. The originality of this work lies in the method’s slicing and merging to enable the sequential processing of high resolution bridge surface textures with a state of the art classifier to distinguish between healthy and potentially unhealthy surface texture. Morphological operators are then used to generate an outline mask to highlight the classification results in the surface texture. The training and validation set consists of 1028 images taken from multiple Department of Transportation bridge inspection databases and data collection from ten highway bridges around Cambridge. The presented method achieves a search space reduction for an inspector of 90.1% with a risk of missing a defect patch of 8.2%. This work is of great significance for bridge inspectors as they are now able to spend more time on assessing potentially unhealthy surface regions instead of searching for these needles in a mainly healthy concrete surface haystack. [ABSTRACT FROM AUTHOR]

This paper presents the suitability of precast components for standardized UK bridges. The conventional design and construction of UK bridges is often criticized for being inefficient and unsafe as the majority of the work is carried out on-site, which requires lots of time and temporary works. The concept of Design for Manufacture and Assembly (DfMA) is employed in this study to overcome the limitations of the current bridge construction practice. First, underlying DfMA criteria for bridge construction are identified and a suitability analysis of precast components based on the identified DfMA criteria identified is conducted via an interview. Second, a case study on a bridge recently built for a highway bridge project is conducted to identify the feasibility of the potential precast components selected from the suitability analysis. The result of the case study demonstrates that the recommended precast components can be successfully used for future standardized bridges of the UK. [ABSTRACT FROM AUTHOR]

Abstract: A roofing contractor typically needs to acquire as-built dimensions of a roof structure several times over the course of its build because a structure is never built to the exact drawing dimensions. In the construction phase and in order to digitally fabricate sheet metal roof panels, the contractor has to measure end-to-end dimensions of boundaries of every roof plane with a certain level of accuracy (i.e., errors less than ±2cm). This is necessary to be able to cut sheet metal coil such that different pieces perfectly fit together. Obtaining these measurements using the exiting roof surveying methods could be costly in terms of equipment, labor, and/or worker exposure to safety hazards. This paper presents a video-based surveying framework as an alternative method which is simple to use, automated, less expensive, and safe. When using this framework, the contractor collects video streams with a calibrated stereo camera set. The captured data is processed to automatically generate a 3D wire-diagram of the target roof. Measurements from the wire-diagram are saved in a digital file (XML format) which could be loaded into an on-site sheet metal folding and cutting machine. Experimental analyses demonstrate applicability of the proposed framework. [Copyright &y& Elsevier]

Abstract: The current procedures in post-earthquake safety and structural assessment are performed manually by a skilled triage team of structural engineers/certified inspectors. These procedures, and particularly the physical measurement of the damage properties, are time-consuming and qualitative in nature. This paper proposes a novel method that automatically detects spalled regions on the surface of reinforced concrete columns and measures their properties in image data. Spalling has been accepted as an important indicator of significant damage to structural elements during an earthquake. According to this method, the region of spalling is first isolated by way of a local entropy-based thresholding algorithm. Following this, the exposure of longitudinal reinforcement (depth of spalling into the column) and length of spalling along the column are measured using a novel global adaptive thresholding algorithm in conjunction with image processing methods in template matching and morphological operations. The method was tested on a database of damaged RC column images collected after the 2010 Haiti earthquake, and comparison of the results with manual measurements indicate the validity of the method. [Copyright &y& Elsevier]

Abstract: The commercial far-range (>10m) spatial data collection methods for acquiring infrastructure’s geometric data are not completely automated because of the necessary manual pre- and/or post-processing work. The required amount of human intervention and, in some cases, the high equipment costs associated with these methods impede their adoption by the majority of infrastructure mapping activities. This paper presents an automated stereo vision-based method, as an alternative and inexpensive solution, to producing a sparse Euclidean 3D point cloud of an infrastructure scene utilizing two video streams captured by a set of two calibrated cameras. In this process SURF features are automatically detected and matched between each pair of stereo video frames. 3D coordinates of the matched feature points are then calculated via triangulation. The detected SURF features in two successive video frames are automatically matched and the RANSAC algorithm is used to discard mismatches. The quaternion motion estimation method is then used along with bundle adjustment optimization to register successive point clouds. The method was tested on a database of infrastructure stereo video streams. The validity and statistical significance of the results were evaluated by comparing the spatial distance of randomly selected feature points with their corresponding tape measurements. [Copyright &y& Elsevier]

Abstract: Tracking of project related entities such as construction equipment, materials, and personnel is used to calculate productivity, detect travel path conflicts, enhance the safety on the site, and monitor the project. Radio frequency tracking technologies (Wi-Fi, RFID, UWB) and GPS are commonly used for this purpose. However, on large-scale sites, deploying, maintaining and removing such systems can be costly and time-consuming. In addition, privacy issues with personnel tracking often limits the usability of these technologies on construction sites. This paper presents a vision based tracking framework that holds promise to address these limitations. The framework uses videos from a set of two or more static cameras placed on construction sites. In each camera view, the framework identifies and tracks construction entities providing 2D image coordinates across frames. Combining the 2D coordinates based on the installed camera system (the distance between the cameras and the view angles of them), 3D coordinates are calculated at each frame. The results of each step are presented to illustrate the feasibility of the framework. [Copyright &y& Elsevier]

Abstract: Pavement condition assessment is essential when developing road network maintenance programs. In practice, the data collection process is to a large extent automated. However, pavement distress detection (cracks, potholes, etc.) is mostly performed manually, which is labor-intensive and time-consuming. Existing methods either rely on complete 3D surface reconstruction, which comes along with high equipment and computation costs, or make use of acceleration data, which can only provide preliminary and rough condition surveys. In this paper we present a method for automated pothole detection in asphalt pavement images. In the proposed method an image is first segmented into defect and non-defect regions using histogram shape-based thresholding. Based on the geometric properties of a defect region the potential pothole shape is approximated utilizing morphological thinning and elliptic regression. Subsequently, the texture inside a potential defect shape is extracted and compared with the texture of the surrounding non-defect pavement in order to determine if the region of interest represents an actual pothole. This methodology has been implemented in a MATLAB prototype, trained and tested on 120 pavement images. The results show that this method can detect potholes in asphalt pavement images with reasonable accuracy. [Copyright &y& Elsevier]

Abstract: Only very few constructed facilities today have a complete record of as-built information. Despite the growing use of Building Information Modelling and the improvement in as-built records, several more years will be required before guidelines that require as-built data modelling will be implemented for the majority of constructed facilities, and this will still not address the stock of existing buildings. A technical solution for scanning buildings and compiling Building Information Models is needed. However, this is a multidisciplinary problem, requiring expertise in scanning, computer vision and videogrammetry, machine learning, and parametric object modelling. This paper outlines the technical approach proposed by a consortium of researchers that has gathered to tackle the ambitious goal of automating as-built modelling as far as possible. The top level framework of the proposed solution is presented, and each process, input and output is explained, along with the steps needed to validate them. Preliminary experiments on the earlier stages (i.e. processes) of the framework proposed are conducted and results are shown; the work toward implementation of the remainder is ongoing. [ABSTRACT FROM AUTHOR]

Abstract: The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of image processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based construction site image retrieval method is presented. This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content. The results demonstrate the suitability of this method for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images. [Copyright &y& Elsevier]

Shape segmentation from point cloud data is a core step of the digital twinning process for industrial facilities. However, it is also a very labor intensive step, which counteracts the perceived value of the resulting model. The state-of-the-art method for automating cylinder detection can detect cylinders with 62% precision and 70% recall, while other shapes must then be segmented manually and shape segmentation is not achieved. This performance is promising, but it is far from drastically eliminating the manual labor cost. We argue that the use of class segmentation deep learning algorithms has the theoretical potential to perform better in terms of per point accuracy and less manual segmentation time needed. However, such algorithms could not be used so far due to the lack of a pre-trained dataset of laser scanned industrial shapes as well as the lack of appropriate geometric features in order to learn these shapes. In this paper, we tackle both problems in three steps. First, we parse the industrial point cloud through a novel class segmentation solution (CLOI-NET) that consists of an optimized PointNET++ based deep learning network and post-processing algorithms that enforce stronger contextual relationships per point. We then allow the user to choose the optimal manual annotation of a test facility by means of active learning to further improve the results. We achieve the first step by clustering points in meaningful spatial 3D windows based on their location. Then, we apply a class segmentation deep network, and output a probability distribution of all label categories per point and improve the predicted labels by enforcing post-processing rules. We finally optimize the results by finding the optimal amount of data to be used for training experiments. We validate our method on the largest richly annotated dataset of the most important to model industrial shapes (CLOI) and yield 82% average accuracy per point, 95.6% average AUC among all classes and estimated 70% labor hour savings in class segmentation. This proves that it is the first to automatically segment industrial point cloud shapes with no prior knowledge at commercially viable performance and is the foundation for efficient industrial shape modeling in cluttered point clouds. [ABSTRACT FROM AUTHOR]

The automation of digital twinning for existing reinforced concrete bridges from point clouds remains an unresolved problem. Whilst current methods can automatically detect bridge objects in point clouds in the form of labelled point clusters, the fitting of accurate 3D shapes to point clusters remains largely human dependent largely. 95% of the total manual modelling time is spent on customizing shapes and fitting them correctly. The challenges exhibited in the fitting step are due to the irregular geometries of existing bridges. Existing methods can fit geometric primitives such as cuboids and cylinders to point clusters, assuming bridges are comprised of generic shapes. However, the produced geometric digital twins are too ideal to depict the real geometry of bridges. In addition, none of the existing methods have explicitly demonstrated how to evaluate the resulting Industry Foundation Classes bridge data models in terms of spatial accuracy using quantitative measurements. In this article, we tackle these challenges by delivering a slicing-based object fitting method that can generate the geometric digital twin of an existing reinforced concrete bridge from four types of labelled point cluster. The quality of the generated models is gauged using cloud-to-cloud distance-based metrics. Experiments on ten bridge point cloud datasets indicate that the method achieves an average modelling distance of 7.05 cm (while the manual method achieves 7.69 cm), and an average modelling time of 37.8 s. This is a huge leap over the current practice of digital twinning performed manually. • An object fitting method that can digitally twin bridges is proposed. • The method can rapidly twin bridge concrete elements. • Local configurations offer characterization to approximate the global topology. • The resulting geometric digital twins are evaluated using quantitative metrics. [ABSTRACT FROM AUTHOR]

Abstract Monitoring of construction workers is important in managing labour productivity. To date, the construction sector relies either on intensive manual observations or intrusive tag-based practices. Visual tracking methods can provide automated and tag-less monitoring. However, no method to date has succeeded in tracking multiple workers, as construction sites are complex environments due to congestion, background clutter and occlusions. In addition, workers have similar appearance and exhibit illumination/scale/posture variations and abrupt changes in movement over the course of their task. To address these shortcomings, we propose a vision-based method that consists of 3 models. Firstly, an adaptive model provides continuous information about the previous position of workers and their appearance features. Secondly, a prediction model is used to calculate the current position of workers, and finally, an appearance model provides accurate localisation. Experimental results show that the proposed method achieves high performance and outperforms the latest relevant state of the art methods. Highlights • Current practices cannot provide an efficient monitoring of construction workers. • The proposed vision-based method adapts to several challenges e.g. congestion, occlusions. • The proposed vision-based method can truck multiple workers simultaneously. • A prediction, appearance, filtering and adaptive model are combined together in a novel way. [ABSTRACT FROM AUTHOR]

Pavement crack tracing is paramount to missions encompassing automated crack sealing for road maintenance. However, existing methods still face several challenges, including the incapability to precisely extract crack trajectories and the challenge of tuning control parameters within intricate backgrounds. To address these limitations, the ViT-S2T network and the ELM-PID control system are proposed for crack tracing. Specifically, the ViT-S2T consists of two branches. The transformer-based feature extraction module (TFEM) integrates multi-head attention mechanism and multi-layer perceptron to capture global contextual crack semantic features. The incoherent segmentation masks (ISM) employs a binary classifier to predict the coarsest irrelevant mask and further performs up-sampling fusion of higher-resolution features. Moreover, the Neural-PID control method is designed to track crack trajectories, combining Extreme Learning Machines (ELM) and Proportional Integral Derivative (PID). The ELM-PID controller utilizes a three-layer backpropagation neural network and proposes the ELM model for adaptive adjustment by predicting the tuning parameters of PID. This framework is applied to real-time visual tracing for edge AI. Extensive tests performed on three arduous datasets of DeepCrack, CFD, and S2TCrack, achieving a precision of 82.76% and mAP@0.5 of 75.63% and speed of 0.0513 m/s, demonstrating the superior and robust nature of our approach in pavement crack tracing. • A robust pavement crack tracing control framework is proposed for crack tracing and repair. • A novel network termed ViT-S2T was developed to ensure finer segmentation of the trajectory of cracks. • The proposed ELM-PID control leveraged the self-learning capabilities of the ELM to allow robust crack. • Our framework was implemented on an Autonomous Mobile Robot to achieve crack tracing in the real road pavement. [ABSTRACT FROM AUTHOR]

Amid escalating climate change and extreme weather events, urban road networks face increasingly severe flood risks. Current congestion risk analyses on transportation systems, largely grounded in static network characteristics, fall short of capturing the dynamic impacts of evolving flood events on network topology. A notable gap in integrating dynamic flood models with detailed temporal network property measurements impedes a deep understanding of the cascading mechanisms of traffic disruptions caused by floods. The research introduces an innovative framework for assessing dynamic congestion risks within transportation networks under flooding conditions. This framework comprises four distinct layers. The Data and Context layer collates and contextualizes primary data, encompassing road networks, commuter behaviour patterns and hydrological risks. This layer establishes a baseline for understanding the structure of the transportation system and its environmental vulnerabilities. The Computation layer focuses on calculating context-based betweenness centrality values to capture the importance of road segments within the network, setting a static indicator. Concurrently, this layer examines the dynamic exposure flag under flood conditions, providing a dynamic indicator that accounts for the fluctuating susceptibility of road networks to flooding. The Integration layer bridges the topological analysis and the functional performance of the transportation system. It creates a dynamic series that describes the topological properties of the network, reflecting its vulnerability to disruptions. Simultaneously, the time-variant traffic demand series are tracked to capture the system's operational response to flood-induced conditions. Finally, the Evaluation layer synthesizes data and analyses from the previous modules. This synthesis involves a novel dual assessment: generating a time-variant vulnerability index series and evaluating the time-variant congestion risk map on the transportation system under flooding. The London case study is developed based on this proposed framework to identify critical areas of congestion risks. This study offers valuable data-driven insights for urban planning and disaster management, aiding policymakers, planners and emergency teams to enhance urban resilience against flood-induced transport disruptions. [ABSTRACT FROM AUTHOR]

Abstract: Videogrammetry is an inexpensive and easy-to-use technology for spatial 3D scene recovery. When applied to large scale civil infrastructure scenes, only a small percentage of the collected video frames are required to achieve robust results. However, choosing the right frames requires careful consideration. Videotaping a built infrastructure scene results in large video files filled with blurry, noisy, or redundant frames. This is due to frame rate to camera speed ratios that are often higher than necessary; camera and lens imperfections and limitations that result in imaging noise; and occasional jerky motions of the camera that result in motion blur; all of which can significantly affect the performance of the videogrammetric pipeline. To tackle these issues, this paper proposes a novel method for automating the selection of an optimized number of informative, high quality frames. According to this method, as the first step, blurred frames are removed using the thresholds determined based on a minimum level of frame quality required to obtain robust results. Then, an optimum number of key frames are selected from the remaining frames using the selection criteria devised by the authors. Experimental results show that the proposed method outperforms existing methods in terms of improved 3D reconstruction results, while maintaining the optimum number of extracted frames needed to generate high quality 3D point clouds. [Copyright &y& Elsevier]

Abstract: Estimating the fundamental matrix (F), to determine the epipolar geometry between a pair of images or video frames, is a basic step for a wide variety of vision-based functions used in construction operations, such as camera-pair calibration, automatic progress monitoring, and 3D reconstruction. Currently, robust methods (e.g., SIFT+normalized eight-point algorithm+RANSAC) are widely used in the construction community for this purpose. Although they can provide acceptable accuracy, the significant amount of required computational time impedes their adoption in real-time applications, especially video data analysis with many frames per second. Aiming to overcome this limitation, this paper presents and evaluates the accuracy of a solution to find F by combining the use of two speedy and consistent methods: SURF for the selection of a robust set of point correspondences and the normalized eight-point algorithm. This solution is tested extensively on construction site image pairs including changes in viewpoint, scale, illumination, rotation, and moving objects. The results demonstrate that this method can be used for real-time applications (5 image pairs per second with the resolution of 640×480) involving scenes of the built environment. [Copyright &y& Elsevier]

Significant funds have been allocated to maintain road networks each year in developed countries. Performance prediction is crucial for pavement management systems to adjust working plans and budget allocation. As a Bayesian nonparametric method, Gaussian process regression (GPR) is powerful in predicting nonlinear time series and quantifying uncertainty. However, it remains computationally intensive and fails to adapt to the time-varying characteristics. To address such issues, a dynamic GPR model is proposed for probabilistic prediction of the International Roughness Index (IRI) for flexible pavements. A moving window strategy is developed to substantially shrink the size of training data, which effectively alleviates computational cost and thus leads to a dynamic GPR. A genetic algorithm is then adopted to determine the optimal window size by considering the trade-off between computational efficiency and accuracy. A dataset acquired from Long-Term Pavement Performance (LTPP) is used to demonstrate the feasibility of the dynamic GPR. Its performance is compared to traditional GPR as well as dynamic and static Bayesian linear regression (BLR) models. The comparison results indicate that the proposed dynamic GPR can increase the accuracy by 0.86, 1.52, and 2.27 times for dynamic BLR, static GPR, and static BLR, respectively. It exhibits the best results in terms of accuracy and uncertainty metrics due to its nonlinear modelling and time-varying ability. • A dynamic Gaussian process regression (GPR) model is proposed for probabilistic prediction of pavement conditions. • A moving window strategy is developed to effectively alleviate the computational cost. • A genetic algorithm is adopted to determine the optimal window size. • The traditional GPR as well as dynamic and static Bayesian linear regression models are employed for comparison. [ABSTRACT FROM AUTHOR]

Classifying concrete defects during a bridge inspection remains a subjective and laborious task. The risk of getting a false result is approximately 50% if different inspectors assess the same concrete defect. This is significant in the light of an over-aging bridge stock, decreasing infrastructure maintenance budgets and catastrophic bridge collapses as happened in 2018 in Genoa, Italy. To support an automated inspection and an objective bridge defect classification, we propose a three-staged concrete defect classifier that can multi-classify potentially unhealthy bridge areas into their specific defect type in conformity with existing bridge inspection guidelines. Three separate deep neural pre-trained networks are fine-tuned based on a multi-source dataset consisting of self-collected image samples plus several Departments of Transportation inspection databases. We show that this approach can reliably classify multiple defect types with an average mean score of 85%. Our presented multi-classifier is a contribution towards developing a mostly or fully inspection schema for a more cost-effective and more objective bridge inspection. • The presented method can automatically multi-classify concrete bridge defects on image patches in accordance with existing inspection guidelines. • The cross-learning strategy of using a pre-trained network and refine this to domain-specific knowledge can be successfully applied for concrete bridge surface defects. • The multi-classifier can adapt to local variations and consider possible (and impossible) defect combinations. [ABSTRACT FROM AUTHOR]

• The practice of green retrofitting of existing buildings (GREB) is no more an option, it is an absolute necessity. • GREB has received significant attention from sholars and practitioners, with the proliferation of research publications. • This study analyzes the existing global research on GREB through the lens of a scientometric review technique. • A holistic GREB framework involving two potential pathways was proposed. • This study act as a catalyst to spur the practice by revealing the research needs in this field. Green retrofitting of existing buildings (GREB) represents one of the best opportunities to reduce global energy consumption and carbon emissions, as well as improve people's health, well-being, and quality of life. It has thus received significant attention from researchers and practitioners over the years, with the proliferation of research works and publications. However, no study has attempted to comprehensively investigate the cumulative theoretical and practical value of this body of knowledge. This study aims to analyze the existing global research on GREB through the lens of a scientometric review technique and to provide pathways for future research. The scientometric mapping technique was utilized to systematically and quantitatively analyze 5,288 related bibliographic records retrieved from Scopus. Results showed that "energy efficiency" retrofitting has become the utmost research priority, while "occupant behaviour" and "indoor environmental quality" (comfort, satisfaction, air quality, etc.) are declining in importance in the current framework of GREB. Moreover, other concepts such as intelligent buildings, circular economy, and nearly Zero Energy Building (nZEB) are infusing into GREB. Based on the status-quo of practice, a holistic GREB framework involving two potential pathways – technology/approach-centred and human/physical-centred pathways – was proposed. The findings of this study not only contribute to the knowledge domain, but also act as a catalyst to spur the practice by revealing the research needs in this field. [ABSTRACT FROM AUTHOR]