Utilising Convolutional Neural Networks for Pavement Distress Classification and Detection

This paper examines deep learning models for accurate and efficient identification and classification of pavement distresses. In it, a variety of related studies conducted on the topic as well as the various identification and classification methods proposed, such as edge detection, machine learning classification informed by statistical feature extraction, artificial neural networks, and real-time object detection systems, are discussed. The study investigates the effect of image processing techniques such as grayscaling, background subtraction, and image resizing on the performance and generalizability of the models. Using convolutional neural networks (CNN) architectures, this paper proposes a model that correctly classifies images into five pavement distress categories, namely fatigue (or alligator), longitudinal, transverse, patches, and craters, with an accuracy rate of 90.4% and a recall rate of 90.1%. The model is contrasted to a current state-of-the-art model based on the You Only Look Once framework as well as a baseline CNN model to demonstrate the impact of the image processing and architecture building techniques discussed on performance. The findings of this paper contribute to the fields of computer vision and infrastructure monitoring by demonstrating the efficacy of convolutional neural networks (CNNs) in image classification and the viability of using CNNbased models to automate pavement condition monitoring

FRACTURE AND FATIGUE CHARACTERIZATION OF STABILIZED SOILS AND SULFUR-EXTENDED ASPHALT USING MONOTONIC AND CYCLIC SEMI-CIRCULAR BENDING TESTS

The primary goal of this research is to develop test methodologies to characterize fracture and fatigue performances of chemically stabilized soils and asphalt mixtures. The fracture resistance of a chemically stabilized base or subbase layer is important for durability and sustainability of the pavement structure. Thus, an appropriate test protocol to characterize the fracture resistance of the stabilized bases, subbases and subgrade soils is essential for design of pavement materials and structures. This research proposed a protocol based on the semi-circular bending (SCB) test to measure fracture resistance (i.e., fracture energy and fracture toughness) of the chemically stabilized material (CSM). The effects of three test variables including specimen thickness, notch length and loading rate on fracture properties were investigated, and appropriate values for these test variables were selected for the SCB test protocol. The proposed SCB test method was successful in characterizing the fracture resistance of three different CSMs. In order to more definitively address fracture properties of the CSM three-dimensional cohesive zone modeling was used and the simulations agreed very well with the experimental results. Both of the fracture properties obtained from the experiment and the cohesive zone modeling indicated that polymer-stabilized limestone exhibited a much higher fracture resistance than cement-stabilized limestone and cement-stabilized sand. In order to characterize crack growth of CSMs, a compliance method based on the cyclic SCB test was proposed, which was successfully used to characterize crack growth rate of cement stabilized materials. This method is promising as it shows much higher coefficients of correlation when fitting the data to the Paris’ law equation. Characterizing fracture behavior and crack propagation of asphalt mixtures is helpful for optimizing mixture design and predicting cracking performance of asphalt pavements. This research used a digital image correlation (DIC) system to measure the horizontal strain field of a crack tip, which is consistent with the SCB fracture test results. It is observed that the horizontal strain field is more localized at a lower testing temperature and a higher peak load. In addition, this research proposed a new method based on the cyclic semi-circular bending test to characterize crack growth rate of asphalt mixtures. To accurately capture crack length for determining crack growth rate, a DIC is used, and crack mouth opening displacement (CMOD) is measured by linear variable differential transformers. Correlations between crack length and CMOD are established, which are used to determine crack lengths corresponding to loading cycles over the testing process. The proposed cyclic semi-circular bending test successfully characterizes the Paris’s law coefficients of sulfur-extended asphalt mixtures. The cyclic semi-circular bending test provides substantially lower coefficients of variance in terms of cycles to fatigue failure compared with other traditional fatigue tests. Test results were used to determine the impact of sulfur content on fatigue life with the conclusion that a low level of sulfur added in the case of 15% change the rheology (softener) of the asphalt to the degree that more damage is caused in a controlled-stress mode loading. However, an increase in sulfur content (30% and 45%) apparently produces a stiffer mixture that is more resistant to damage and is comparable to the control mixture

Deep Learning Framework For Intelligent Pavement Condition Rating: A direct classification approach for regional and local roads

Transport authorities rely on pavement characteristics to determine a pavement condition rating index. However, manually computing ratings can be a tedious, subjective, time-consuming, and training-intensive process. This paper presents a deep-learning framework for automatically rating the condition of rural road pavements using digital images captured from a dashboard-mounted camera. The framework includes pavement segmentation, data cleaning, image cropping and resizing, and pavement condition rating classification. A dataset of images, captured from diverse roads in Ireland and rated by two expert raters using the pavement surface condition index (PSCI) scale, was created. Deep-learning models were developed to perform pavement segmentation and condition rating classification. The automated PSCI rating achieved an average Cohen Kappa score and F1-score of 0.9 and 0.85, respectively, across 1–10 rating classes on an independent test set. The incorporation of unique image augmentation during training enabled the models to exhibit increased robustness against variations in background and clutter

Artificial Intelligence in Civil Infrastructure Health Monitoring—historical Perspectives, Current Trends, and Future Visions

Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the major technology progression in this research field is provided in a chronological order. Detailed applications, key contributions, and performance measures of each milestone publication are presented. Representative technologies are detailed to demonstrate current research trends. A road map for future research is outlined to address contemporary issues such as explainable and physics-informed AI. This paper will provide readers with a lucid memoir of the historical progress, a good sense of the current trends, and a clear vision for future research

Eleventh International Conference on the Bearing Capacity of Roads, Railways and Airfields

Innovations in Road, Railway and Airfield Bearing Capacity – Volume 2 comprises the second part of contributions to the 11th International Conference on Bearing Capacity of Roads, Railways and Airfields (2022). In anticipation of the event, it unveils state-of-the-art information and research on the latest policies, traffic loading measurements, in-situ measurements and condition surveys, functional testing, deflection measurement evaluation, structural performance prediction for pavements and tracks, new construction and rehabilitation design systems, frost affected areas, drainage and environmental effects, reinforcement, traditional and recycled materials, full scale testing and on case histories of road, railways and airfields. This edited work is intended for a global audience of road, railway and airfield engineers, researchers and consultants, as well as building and maintenance companies looking to further upgrade their practices in the field

New innovations in pavement materials and engineering: A review on pavement engineering research 2021

Sustainable and resilient pavement infrastructure is critical for current economic and environmental challenges. In the past 10 years, the pavement infrastructure strongly supports the rapid development of the global social economy. New theories, new methods, new technologies and new materials related to pavement engineering are emerging. Deterioration of pavement infrastructure is a typical multi-physics problem. Because of actual coupled behaviors of traffic and environmental conditions, predictions of pavement service life become more and more complicated and require a deep knowledge of pavement material analysis. In order to summarize the current and determine the future research of pavement engineering, Journal of Traffic and Transportation Engineering (English Edition) has launched a review paper on the topic of “New innovations in pavement materials and engineering: A review on pavement engineering research 2021”. Based on the joint-effort of 43 scholars from 24 well-known universities in highway engineering, this review paper systematically analyzes the research status and future development direction of 5 major fields of pavement engineering in the world. The content includes asphalt binder performance and modeling, mixture performance and modeling of pavement materials, multi-scale mechanics, green and sustainable pavement, and intelligent pavement. Overall, this review paper is able to provide references and insights for researchers and engineers in the field of pavement engineering

Advances in Asphalt Pavement Technologies and Practices

Unlike other construction materials, road materials have developed minimally over the past 100 years. However, since the 1970s, the focus has been on more sustainable road construction materials such as recycled asphalt pavements. Recycling asphalt involves removing old asphalt and mixing it with new (fresh) aggregates, binders, and/or rejuvenators. Similarly, there are various efforts to use alternative modifiers and technical solutions such as crumb rubber, plastics, or various types of fibres. For the past two decades, researchers have been developing novel materials and technologies, such as self-healing materials, in order to improve road design, construction, and maintenance efficiency and reduce the financial and environmental burden of road construction. This Special Issue on “Advances in Asphalt Pavement Technologies and Practices” curates advanced/novel work on asphalt pavement design, construction, and maintenance. The Special Issue comprises 19 papers describing unique works that address the current challenges that the asphalt industry and road owners face

ELECTRICAL CHARACTERIZATION AND APPLICATIONS OF CONDUCTIVE INFRASTRUCTURE MATERIALS

The feasibility of multifunctional applications of electrically conductive asphalt concrete is investigated. The conductive asphalt concrete has a huge potential for various multifunctional applications such as self-healing, self-sensing, and deicing. This study examines the method of controlling conductivity of asphalt composites, electrical characterization of the composites with alternating current impedance spectroscopy (ACIS), application for damage self-sensing, and application for removing snow and ice on pavements. Aiming to control the electrical conductivity of asphalt concrete with a smooth transition from insulated to conductive phase, nine types of graphite having different particle shape, size, and origin were mixed with asphalt binders, and their effects on imparting conductivity were investigated. The natural flake graphite is effective to mitigate the percolation threshold, and a sufficiently high conductivity (up to 10^2 Ω·cm) can be achieved by replacing a part of fillers only with the graphite. For the electrical characterization of the conductive asphalt composites, ACIS is employed. Through this technique, the equivalent electrical circuits in various levels of conductivity is constructed for the first time. The results show that a specific conductivity range containing 20-25% flake graphite by weight of the asphalt mastic is suitable for sensing applications. Self-sensing of damage is one of probable multifunctional applications of the conductive asphalt concrete, and its feasibility is investigated using ACIS. The experiments with dry and wet conditions show that the real and imaginary impedance increase with the increase of the damage, while the capacitance value does not show a clear relationship with the damage evolution. The results also show that the distance between electrodes is important for measuring damage with ACIS. The feasibility of the heated pavement using the conductive asphalt as a cost-effective and pollution-free solution was investigated. Bench scale slab heating test, non-steady state heat transfer analysis, and life-cycle assessment (LCA) were conducted. The results of these methodologies reveal that the heating capacity of the conductive asphalt is sufficient for deicing on pavement surfaces. The electrical and mechanical data obtained from this study provide essential information on multifunctional applications of conductive asphalt concrete, which will lead to technical innovations for more sustainable pavement systems