Author: Elham Eslami
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task, however, remains challenging due to the high variations in road objects and pavement types, variety of lighting condition, low contrast, and background noises in pavement images. In this dissertation, we propose novel deep learning algorithms for image-based road condition assessment to tackle current challenges in detection, classification and segmentation of pavement images. Motivated by the need for classifying a wide range of objects in road monitoring, this dissertation introduces a Multi-Scale Convolution Neural Network (MCNN) for multi-class classification of pavement images. MCNN improves the classification performance by encoding contextual information through multi-scale input tiles. Then, an Attention-Based Multi-Scale CNN (A+MCNN) is proposed to further improve the classification results through a novel mid-fusion strategy for combining multi-scale features extracted from multi-scale input tiles. An attention module is designed as an adaptive fusion strategy to generate importance scores and integrate multi-scale features based on how informative they are to the classification task. Finally, Dual Attention CNN (DACNN) is introduced to improve the performance of multi-class classification using both intensity and range images collected with 3D laser imaging devices. DACNN integrates information in intensity and range images to enhance distinct features improving the objects classification in noisy images under various illumination conditions. The standard road condition assessment includes determining not only the type of defects but also the severity of detects. In this regard, a pavement crack segmentation algorithm, CrackSegmenter, is proposed to detect crack at pixel level. The CrackSegmenter leverages residual blocks, attention blocks, Atrous Spatial Pyramid Pooling (ASSP), and squeeze and excitation blocks to improve segmentation performance in pavement crack images.
Author: Ju Huyan
Publisher:
ISBN:
Category : Machine learning
Languages : en
Pages : 280
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Book Description
Pavement Management System (PMS) analytical tools mainly consist of pavement condition investigation and evaluation tools, pavement condition rating and assessment tools, pavement performance prediction tools, treatment prioritizations and implementation tools. The effectiveness of a PMS highly depends on the efficiency and reliability of its pavement condition evaluation tools. Traditionally, pavement condition investigation and evaluation practices are based on manual distress surveys and performance level assessments, which have been blamed for low efficiency low reliability. Those kinds of manually surveys are labor intensive and unsafe due to proximity to live traffic conditions. Meanwhile, the accuracy can be lower due to the subjective nature of the evaluators. Considering these factors, semiautomated and automated pavement condition evaluation tools had been developed for several years. In current years, it is undoubtable that highly advanced computerized technologies have resulted successful applications in diverse engineering fields. Therefore, these techniques can be successfully incorporated into pavement condition evaluation distress detection, the analytical tools can improve the performance of existing PMSs. Hence, this research aims to bridge the gaps between highly advanced Machine Learning Techniques (MLTs) and the existing analytical tools of current PMSs. The research outputs intend to provide pavement condition evaluation tools that meet the requirement of high efficiency, accuracy, and reliability. To achieve the objectives of this research, six pavement damage condition and performance evaluation methodologies are developed. The roughness condition of pavement surface directly influences the riding quality of the users. International Roughness Index (IRI) is used worldwide by research institutions, pavement condition evaluation and management agencies to evaluate the roughness condition of the pavement. IRI is a time-dependent variable which generally tends to increase with the increase of the pavement service life. In this consideration, a multi-granularity fuzzy time series analysis based IRI prediction model is developed. Meanwhile, Particle Swarm Optimization (PSO) method is used for model optimization to obtain satisfactory IRI prediction results. Historical IRI data extracted from the InfoPave website have been used for training and testing the model. Experiment results proved the effectiveness of this method. Automated pavement condition evaluation tools can provide overall performance indices, which can then be used for treatment planning. The calculations of those performance indices are required for surface distress level and roughness condition evaluations. However, pavement surface roughness conditions are hard to obtain from surface image indicators. With this consideration, an image indicators-based pavement roughness and the overall performance prediction tools are developed. The state-of-the-art machine learning technique, XGBoost, is utilized as the main method in model training, validating and testing. In order to find the dominant image indicators that influence the pavement roughness condition and the overall performance conditions, the comprehensive pavement performance evaluation data collected by ARAN 900 are analyzed. Back Propagation Neural Network (BPNN) is used to develop the performance prediction models. On this basis, the mean important values (MIVs) for each input factor are calculated to evaluate the contributions of the input indicators. It has been observed that indicators of the wheel path cracking have the highest MIVs, which emphasizes the importance of cracking-focused maintenance treatments. The same issue is also found that current automated pavement condition evaluation systems only include the analysis of pavement surface distresses, without considering the structural capacity of the actual pavement. Hence, the structural performance analysis-based pavement performance prediction tools are developed using the Support Vector Machines (SVMs). To guarantee the overall performance of the proposed methodologies, heuristic methods including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are selected to optimize the model. The experiments results show a promising future of machine learning based pavement structural performance prediction. Automated pavement condition analyzers usually detect pavement surface distress through the collected pavement surface images. Then, distress types, severities, quantities, and other parameters are calculated for the overall performance index calculation. Cracks are one of the most important pavement surface distresses that should be quantified. Traditional approaches are less accurate and efficient in locating, counting and quantifying various types of cracks initialed on the pavement surface. An integrated Crack Deep Net (CrackDN) is developed based on deep learning technologies. Through model training, validation and testing, it has proved that CrackDN can detect pavement surface cracks on complex background with high accuracy. Moreover, the combination of box-level pavement crack locating, and pixel-level crack calculation can achieve comprehensive crack analysis. Thereby, more effective maintenance treatments can be assigned. Hence, a methodology regarding pixel-level crack detection which is called CrackU-net, is proposed. CrackU-net is composed of several convolutional, maxpooling, and up-convolutional layers. The model is developed based on the innovations of deep learning-based segmentation. Pavement crack data are collected by multiple devices, including automated pavement condition survey vehicles, smartphones, and action cameras. The proposed CrackU-net is tested on a separate crack image set which has not been used for training the model. The results demonstrate a promising future of use in the PMSs. Finally, the proposed toolboxes are validated through comparative experiments in terms of accuracy (precision, recall, and F-measure) and error levels. The accuracies of all those models are higher than 0.9 and the errors are lower than 0.05. Meanwhile, the findings of this research suggest that the wheel path cracking should be a priority when conducting maintenance activity planning. Benefiting from the highly advanced machine learning technologies, pavement roughness condition and the overall performance levels have a promising future of being predicted by extraction of the image indicators. Moreover, deep learning methods can be utilized to achieve both box-level and pixel-level pavement crack detection with satisfactory performance. Therefore, it is suggested that those state-of-the-art toolboxes be integrated into current PMSs to upgrade their service levels.
Author: Lelitha Devi
Publisher: Springer Nature
ISBN: 981193505X
Category : Technology & Engineering
Languages : en
Pages : 381
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Book Description
This book comprises the proceedings of the Sixth International Conference of Transportation Research Group of India (CTRG2021) focusing on emerging opportunities and challenges in the field of transportation of people and freight. The contents of the volume include recent advancements in the pavements and materials study like Fatigue damage, Moisture damage prediction, Quantification of Aging of Polymer, and Effect of short-term aging. It also covers rapidly evolving topics like Road network analysis, Location choice analysis for Transit-Oriented Development (TOD), Transit ridership, etc. This book will be beneficial to researchers, educators, practitioners, and policymakers alike.
Author: Luana Lopes Amaral Loures
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Unpaved roads have a significant role in Canada's transportation and service activities, accounting for close to 60% of Canada's total public road networks. Furthermore, they connect agricultural, mining, recreational areas, and small communities to the nearby towns and businesses. An effective maintenance program for a network of unpaved roads requires a detailed assessment of the road surface's condition, and such assessment is usually made by visual inspections which can be time-consuming and error prone. The main part of these evaluations aims to identify distresses on the road surface, such as washboarding (corrugation), potholes, and rutting. Many research studies have developed methods to automate condition assessment of asphalt roads by combining machine learning algorithms and low-cost unmanned aerial vehicles (UAV), but the research on the automated assessment of unpaved roads is very limited. A system has been developed in this study to automate the assessment of unpaved roads by coupling computer vision methods, namely deep convolutional neural networks, and UAV-based imaging. This automated system could be used as an alternative method to reduce the need for human effort and possible manual errors, and therefore improve road maintenance programs in remote areas. The performance of the proposed method was evaluated using different test settings, and despite having some challenges, such as false positives, it showed promising outcomes that can contribute to the proposed purpose of this research. This proposed method has a potential for further improvement and the findings can be used as a basis for similar studies.
Author: M. Z. Naser
Publisher: Elsevier
ISBN: 0128240741
Category : Technology & Engineering
Languages : en
Pages : 300
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Book Description
The past few years have demonstrated how civil infrastructure continues to experience an unprecedented scale of extreme loading conditions (i.e. hurricanes, wildfires and earthquakes). Despite recent advancements in various civil engineering disciplines, specific to the analysis, design and assessment of structures, it is unfortunate that it is common nowadays to witness large scale damage in buildings, bridges and other infrastructure. The analysis, design and assessment of infrastructure comprises of a multitude of dimensions spanning a highly complex paradigm across material sciences, structural engineering, construction and planning among others. While traditional methods fall short of adequately accounting for such complexity, fortunately, computational intelligence presents novel solutions that can effectively tackle growing demands of intense extreme events and modern designs of infrastructure – especially in this era where infrastructure is reaching new heights and serving larger populations with high social awareness and expectations. Computational Intelligence for Analysis, Design and Assessment of Civil Infrastructure highlights the growing trend of fostering the use of CI to realize contemporary, smart and safe infrastructure. This is an emerging area that has not fully matured yet and hence the book will draw considerable interest and attention. In a sense, the book presents results of innovative efforts supplemented with case studies from leading researchers that can be used as benchmarks to carryout future experiments and/or facilitate development of future experiments and advanced numerical models. The book is written with the intention to serve as a guide for a wide audience including senior postgraduate students, academic and industrial researchers, materials scientists and practicing engineers working in civil, structural and mechanical engineering. - Presents the fundamentals of AI/ML and how they can be applied in civil and environmental engineering - Shares the latest advances in explainable and interpretable methods for AI/ML in the context of civil and environmental engineering - Focuses on civil and environmental engineering applications (day-to-day and extreme events) and features case studies and examples covering various aspects of applications
Author: Yong Liu
Publisher: Springer Nature
ISBN: 3030803163
Category : Science
Languages : en
Pages : 153
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Book Description
With the development of social and science, new requirements are put forward for geotechnical engineering. Advanced geotechnical techniques were proposed to solve the new challenges in geotechnical engineering. The articles presented in this volume aim to the new development of geotechnical engineering such as characterization of geomaterials, slope stability, application of environmental protection materials and some other geotechnical issues that are becoming quite relevant in today's world.
Author: Soroush Mokhtari
Publisher:
ISBN:
Category :
Languages : en
Pages : 216
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Book Description
The statistical method used in this study can be employed to avoid subjective crack rating based on human visual inspection. Moreover, the statistical information can be used as fundamental data to justify rehabilitation policies in pavement maintenance. Finally, the application of four classification algorithms, including Artificial Neural Network (ANN), Decision Tree (DT), k-Nearest Neighbours (kNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is investigated for the crack detection framework. The classifiers were evaluated in the following five criteria: 1) prediction performance, 2) computation time, 3) stability of results for highly imbalanced datasets in which, the number of crack objects are significantly smaller than the number of non-crack objects, 4) stability of the classifiers performance for pavements in different deterioration stages, and 5) interpretability of results and clarity of the procedure. Comparison results indicate the advantages of white-box classification methods for computer vision based pavement evaluation. Although black-box methods, such as ANN provide superior classification performance, white-box methods, such as ANFIS, provide useful information about the logic of classification and the effect of feature values on detection results. Such information can provide further insight for the image-based pavement crack detection application.
Author: Fouzi Harrou
Publisher: Elsevier
ISBN: 0128234334
Category : Transportation
Languages : en
Pages : 270
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Book Description
Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. The book examines commonly used traffic analysis methodologies as well the emerging methods that use deep learning methods. Other sections discuss how to understand statistical models and machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems. - Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring - Uses methods based on video and time series data for traffic modeling and forecasting - Includes case studies, key processes guidance and comparisons of different methodologies
Author: Yakoub Bazi
Publisher: MDPI
ISBN: 3036509860
Category : Science
Languages : en
Pages : 438
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Book Description
The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer—at least partially—such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching.
Author: Stefanus Adi Kristiawan
Publisher: Springer Nature
ISBN: 981169348X
Category : Architecture
Languages : en
Pages : 1142
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Book Description
This book is a collection of papers presented at the 5th International Conference on Rehabilitation and Maintenance in Civil Engineering (ICRMCE 2021), held in Surakarta, Indonesia. The papers are grouped into sequential themes representing the structure of this book: o Part 1: Factors affecting building and infrastructure performance o Part 2: Testing and inspection of existing building and infrastructure o Part 3: Protection, maintenance, repair, and retrofitting of building and infrastructure o Part 4: Maintenance management of building and infrastructure o Part 5: Service life modelling of building and infrastructure o Part 6: Hazard mitigation o Part 7: Sustainability aspect in civil engineering design, process, modelling, maintenance, and rehabilitation Postgraduate students, researchers, and practitioners specializing and working in the area of protection, maintenance, repair, and retrofitting of civil engineering infrastructures will find this book very useful.
