Search

Automated visual assessment report generation in structural health monitoring (SHM) offers advantages for building inspections. However, current vision-based approaches that focus primarily on local surface detection cannot be directly used for inspection reports without further interpretation of the detected labels and coordinator metrics for an appropriate serviceability assessment. To address this gap, this paper presents an automated textual assessment framework for retrieving and generating linguistic descriptions of building component images. Six attention-based captioning methods were constructed based on convolutional neural network (Inception-V3, Xception, and ResNet50) and recurrent neural network (GRU, LSTM), and experimented via 7430 pairs of building component images and captions. The results indicated that the proposed methods had good predictive power and ResNet50-LSTM outperformed other methods with average precision, recall, and F1 scores of 0.84, 0.74, and 0.79, respectively. This paper highlights the potential of the image captioning approach for producing accurate and timely periodic structural assessment reports. • An image captioning approach is proposed for automating the structural monitoring health (SMH) of apartment buildings. • Six attention-based encoder-decoder models using CNNs and RNNs architectures were developed for comparative analysis. • A custom dataset of 7430 pairs of image-caption data was extracted from 563 safety inspection reports. • The ResNet50-LSTM model showed great superiority across all evaluation metrics, with an average F1 score of 0.79. • This demonstrates the potential of IC techniques to the current practices of SMH in the construction industry. [ABSTRACT FROM AUTHOR]

Image processing methods for automated concrete crack detection are often challenged by binary noise. Noise removal methods decrease the false positive pixels of crack detection results, often at the cost of a reduction in true positives. This paper proposes a novel method for binary noise removal and segmentation of noisy concrete crack images. The method applies an area threshold before reducing the pixel groups in the image to a skeleton. Each skeleton is connected to its nearest neighbour before the remaining short skeletons in the image are removed using a length threshold. A morphological reconstruction follows to remove all elements in the original noisy image that do not intersect with the skeleton. Finally, pixel groups in close proximity to the endpoints of the pixel groups in the resulting image are reinstated. Testing was conducted on a dataset of noisy binary crack images; the proposed method (Skele-Marker) obtained recall, precision, intersection over union, and F1 score results of 77%, 91%, 72%, and 84%, respectively. Skele-Marker was compared to other methods found in literature and was found to outperform other methods in terms of precision, intersection over union and F1 score. The proposed method is used to make crack detection results more reliable, supporting the ever-growing demand for automated inspections of concrete structures. • We use image processing techniques to remove noise in binary concrete crack images. • False positives in binary concrete crack images can be reduced using our method. • We compare our concrete crack noise removal method to others found in literature. • Our method performed better than other concrete crack segmentation methods. [ABSTRACT FROM AUTHOR]

Precast concrete components are increasingly used in construction. Since the inspection of their quality is important and involves a large workload, automated methods have been researched to improve the efficiency of inspection with sufficient accuracy. This paper presents a review of existing researches on the automated quality inspection of precast concrete components by using scientometric analysis and critical reviews. Firstly, three key research themes, namely, inspection of dimensional quality of components, inspection of surface quality of components, and improvements in data collection and analysis of inspection, are identified and described based on the result of scientometric analysis. Then, existing inspection methods are analyzed and compared through the critical reviews. Next, gaps between existing inspection methods and actual needs are summarized. Finally, future directions in this field are predicted accordingly. Overall, this paper contributes to future researches and applications for the quality inspection of precast concrete components. [Display omitted] • Literatures are reviewed on automated methods for quality inspection of precast concrete components. • Current status of the existing methods is elaborated and summarized. • Gaps for the existing methods with the practical requirements are identified. • It contributes to the further research and development of the relevant techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a line laser scanning thermography system for automated visualization of coating thickness distribution within a steel structure. In the proposed system, a line laser scans the coated steel structure and generates heat energy on the coating surface; the resultant heat response is measured using an infrared camera. Thereafter, the proposed coating thickness visualization algorithm quantifies and visualizes the coating thickness distribution over the entire scanned surface. The proposed system achieves (1) noncontact and nondestructive inspection of invisible coating thickness; (2) automated coating thickness inspection of steel bridges; (3) quantification and visualization of coating thickness via algorithms based on laser-induced heat transfer analyses within the coating layer. The performance of the proposed line laser scanning thermography system was validated through laboratory and field tests. The coating thickness was quantified and visualized with an accuracy of approximately 20 μm. • A line laser scanning thermography system is proposed for coating thickness visualization. • The system achieves automated coating thickness quantification and visualization. • The system enables noncontact, nondestructive inspection of coated steel members. • The performance of the system is validated via laboratory and field tests. • The system achieves an accuracy of 20 μm. [ABSTRACT FROM AUTHOR]

Abstract: Accurate pipeline condition assessment is vital to developing a cost effective and sustainable buried asset management system. Maintenance and rehabilitation of pipeline systems pose a major challenge for most municipalities in North America given their budgetary constraints, demand on providing quality service, and the need for preserving their pipeline infrastructure. This paper presents the proof-of-concept for an automated buried pipeline condition assessment system that can provide additional depth perception of defects in addition to surface assessments provided by current technologies. An ultrasound acoustics-based methodology is proposed to acquire depth perception and complement 2-D crack features available from the SSET camera for inspection of concrete pipes. Experimental results show that the ultrasound immersion scanning and C-Scan Imaging provides rich data for building a reliable defect detection system. The proposed automated inspection system can lead to overcoming many limitations of the current manual inspection practice (i.e. subjecting defect rating system) and can provide a more accurate assessment of buried pipe conditions. [Copyright &y& Elsevier]

Abstract: Pipeline infrastructure is decaying at an accelerating rate due to reduced funding and insufficient quality control resulting in poor installation, little or no inspection and maintenance, and a general lack of uniformity and improvement in design, construction and operation practices. The current practice that is being followed to inspect the conditions of pipes is usually time consuming, tedious and expensive. It may also lead to diagnostic errors due to lack of concentration of human operators. Buried pipe defect classification is thus a practical and important pattern classification problem. These defects appear in the form of randomly shaped cracks and holes, broken joints and laterals, and others. This paper proposes a new neuro-fuzzy classifier that combines neural networks and concepts of fuzzy logic for the classification of defects by extracting features in segmented buried pipe images. A comparative evaluation of the K-NN, fuzzy K-NN, conventional backpropagation network, and proposed neuro-fuzzy projection network classifiers is carried out. Among the five neural methods implemented and tested, the proposed neuro-fuzzy classifier performs the best, with classification accuracies around 90% on real concrete pipe images. [Copyright &y& Elsevier]

Abstract: The detection of cracks in concrete infrastructure is a problem of great interest. In particular, the detection of cracks in buried pipes is a crucial step in assessing the degree of pipe deterioration for municipal and utility operators. The key challenge is that whereas joints and laterals have a predictable appearance, the randomness and irregularity of cracks make them difficult to model. Our previous work has led to a segmented pipe image (with holes, joints, and laterals eliminated) obtained by a morphological approach. This paper presents the development of a statistical filter for the detection of cracks in the pipes. We propose a two-step approach. The first step is local and is used to extract crack features from the buried pipe images; we present two such detectors as well as a method for fusing them. The second step is global and defines the cracks among the segment candidates by processes of cleaning and linking. The influences of the parameters on crack detection are studied and results are presented for various pipe images. [Copyright &y& Elsevier]

Abstract: The enormity of the problem of deteriorating pipeline infrastructure is widely apparent. Since a complete rebuilding of the piping system is not financially realistic, municipal and utility operators require the ability to monitor the condition of buried pipes. Thus, reliable pipeline assessment and management tools are necessary to develop long term cost effective maintenance, repair, and rehabilitation programs. In this paper a simple, robust and efficient image segmentation algorithm for the automated analysis of scanned underground pipe images is presented. The algorithm consists of image pre-processing followed by a sequence of morphological operations to accurately segment pipe cracks, holes, joints, laterals, and collapsed surfaces, a crucial step in the classification of defects in underground pipes. The proposed approach can be completely automated and has been tested on five hundred scanned images of buried concrete sewer pipes from major cities in North America. [Copyright &y& Elsevier]

We propose a novel approach for surface finish classification of digitally fabricated structures using an industrial depth camera. Data collected at different viewpoints are jointly processed to derive the spatial distribution of features describing the reflectance, which is in turn related to the surface finish. The features can be used to classify the surfaces according to their finish e.g., for assessing the homogeneity or conformance. We apply the method to four sprayed plaster specimens of similar visual appearance but different roughness. Using nearest neighbor classification we achieve an accuracy of 97% for the plaster samples. The approach is a contribution towards real-time quality inspection in digital fabrication. • A novel approach for surface finish evaluation of digitally fabricated structures. • Analysis of features related to surface finish derived from depth camera data. • Demonstration on specimens of similar visual appearance but different roughness. • Contribution towards real-time surface quality inspection in digital fabrication. • Non-contact and non-destructive approach for evaluating freshly produced surfaces. [ABSTRACT FROM AUTHOR]

This survey presents an in-depth overview of the last 25 years of research within the field of image-based automation of Closed-Circuit Television (CCTV) and Sewer Scanner and Evaluation Technology (SSET) sewer inspection. The survey investigates both the algorithmic pipeline, and the datasets and corresponding evaluation protocols. As a result of the in-depth survey, several trends within the research field are revealed, discussed, and future research directions are proposed. Based on the conducted survey, we put forth a set of three recommendations, which we believe will further improve and open the research field, as well as make the future research more reproducible: 1) The introduction of free and public benchmark datasets, 2) Standardized evaluation metrics, and 3) Open-sourcing the associated code. Unlabelled Image • Reviewed 113 articles on image-based automated sewer inspection methodologies • Categorized and reviewed the different stages of the algorithmic pipeline • Categorized and reviewed the employed datasets and evaluation protocols • Found clear trends regarding the choice of methodology as a function of time • Calls for the introduction of standardized metrics, and open benchmark datasets [ABSTRACT FROM AUTHOR]