Article
Environmental Sciences
Danyu Wang, Zhen Liu, Xingyu Gu, Wenxiu Wu, Yihan Chen, Lutai Wang
Summary: In this study, an improved YOLOv3 object detection model was proposed for intelligent and accurate measurement of pavement surface potholes. By combining data augmentation and structure optimization, the proposed model showed significant improvement compared to the original YOLOv3 model and demonstrated good robustness.
Article
Chemistry, Analytical
Boris Bucko, Eva Lieskovska, Katarina Zabovska, Michal Zabovsky
Summary: This study focuses on automatic detection of road potholes using Yolo v3 model. It investigates the impact of adverse conditions on pothole detection and develops a dataset with images recorded under different light and weather conditions.
Article
Engineering, Civil
Ahmed Abed, Mujib Rahman, Nick Thom, David Hargreaves, Linglin Li, Gordon Airey
Summary: This study analyzes the formation of potholes and their relationship with other distress types and severity, and develops a simple tool to predict the number of potholes that might appear in a road network based on the network condition. The results demonstrate that potholes are significantly concentrated in sections with deteriorated conditions and it is possible to predict the number of potholes using spatial density as a function of different condition indicators.
TRANSPORTATION RESEARCH RECORD
(2023)
Review
Engineering, Civil
Pranav R. T. Peddinti, Harish Puppala, Byungmin Kim
Summary: The main objective of this study is to provide essential knowledge on the prevalent challenges of existing monitoring techniques and discuss the potential advantages of UAVs over conventional pavement monitoring practice. The study presents a state-of-the-art review emphasizing UAV technicalities in the context of image-based pavement monitoring, and provides a detailed workflow and checklist for drone deployment to ensure safe and high-quality data acquisition for novice users. Finally, the present challenges and future scope of UAV-based pavement monitoring are discussed.
JOURNAL OF TRANSPORTATION ENGINEERING PART B-PAVEMENTS
(2023)
Article
Construction & Building Technology
Raheb Hafezzadeh, Federico Autelitano, Felice Giuliani
Summary: This study focuses on the evaluation of the performance of cold mix patching materials (CMPMs) by proposing innovative solutions derived from pre-existing methodologies. The research identifies several parameters indicative of CMPMs' structural and functional performance, optimizing the mix design and quality assurance/ quality control (QA/QC) process. The findings suggest that new testing methods, such as Hubbard-Field and indentation stability tests, provide more accurate stability assessments for CMPMs. Additionally, customized test methods for raveling potential, bonding properties, and workability enhance the relevance of laboratory findings to practical applications.
CASE STUDIES IN CONSTRUCTION MATERIALS
(2023)
Article
Construction & Building Technology
Jinchao Guan, Xu Yang, Ling Ding, Xiaoyun Cheng, Vincent C. S. Lee, Can Jin
Summary: An automated pixel-level pavement distress detection framework integrating stereo vision and deep learning is developed in this study, which establishes multi-feature pavement image datasets based on a multi-view stereo imaging system and proposes a modified U-net deep learning architecture introducing depthwise separable convolution for efficient crack and pothole segmentation. The results show that the 3D pavement image achieves millimeter-level accuracy, and the enhanced 3D crack segmentation model outperforms other models in terms of accuracy and speed, enabling high-precision automated pothole volume measurement.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Green & Sustainable Science & Technology
Tao Wang, Yasin Amara Sekou S. Dra, Xiaopei Cai, Zhiqiang Cheng, De Zhang, Yi Lin, Huayang Yu
Summary: This paper reviews the state-of-the-art advanced Cold Patching Materials (CPMs) for more effective pothole repair on asphalt pavements in adverse conditions. Three categories of advanced CPMs, including durable CPMs through binder modification, sustainable CPMs considering the use of alternatives, and less toxic CPMs considering the use of alternative solvents, are discussed. The challenges and opportunities for future studies on CPMs, such as exploring novel polymer binders and developing appropriate evaluation methods, are summarized as well.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Engineering, Civil
Yu Qiao, Sikai Chen, Majed Alinizzi, Miltos Alamaniotis, Samuel Labi
Summary: This study used statistical and machine learning techniques to analyze data from in-service pavements in a Midwestern US state, confirming that reliable IRI estimation can be achieved based on distress types, densities, and severities. The results also indicated that estimated IRI is influenced by pavement type and functional class.
JOURNAL OF INFRASTRUCTURE SYSTEMS
(2022)
Article
Construction & Building Technology
Niannian Wang, Jiaxiu Dong, Hongyuan Fang, Bin Li, Kejie Zhai, Duo Ma, Yibo Shen, Haobang Hu
Summary: A low cost and automatic 3D reconstruction and segmentation system for potholes is proposed in this study. It consists of pavement pothole Structure-from-motion (PP-SFM) and a 3D point cloud segmentation network. PP-SFM is used for 3D reconstruction of multi-view 2D pothole images, and Trans-3DSeg with transformer module is developed for effective segmentation of 3D point cloud data. Experimental results show that the proposed system has better segmentation performance with low cost.
CONSTRUCTION AND BUILDING MATERIALS
(2023)
Review
Construction & Building Technology
Raheb Hafezzadeh, Federico Autelitano, Felice Giuliani
Summary: This article provides a critical comparison between using Cold Mix Patching Materials (CMPMs) and Hot Mix Asphalt (HMA) in repairing potholes, covering various aspects such as repair techniques, productivity, and costs. By investigating the impact of crucial factors on the performance of CMPMs, it is noted that there is a growing worldwide interest in these solutions that initially started as fragmented industrial initiatives.
CONSTRUCTION AND BUILDING MATERIALS
(2021)
Article
Construction & Building Technology
Runhua Zhang, Weiguang Zhang, Shihui Shen, Shenghua Wu, Yiming Zhang
Summary: The study investigates the correlations between laboratory measured binder/mixture properties and actual pavement cracking performance, identifying specific parameters with strong correlations to fatigue and thermal cracking on the field, proposing preliminary threshold values to minimize and control asphalt mixtures cracking issues.
CONSTRUCTION AND BUILDING MATERIALS
(2021)
Article
Construction & Building Technology
Xiaolong Wu, Jun Zhang, Yize Xie, Ming Yao, Zhiguo Jiang
Summary: Timely repair of potholes is important to control pavement deterioration and minimize traffic adverse impacts. In this study, a series of fast-curing two-component polyurethane (PU) binders with varying hard segment content (HSC) were prepared and used to prepare PU mixtures (PUMs) through mixing with aggregates. The results showed that the prepared PUs had good mechanical properties and water resistance, with increased tensile strength and stability with higher HSC content. However, the raveling resistance deteriorated with increasing HSC.
JOURNAL OF MATERIALS IN CIVIL ENGINEERING
(2023)
Article
Construction & Building Technology
Wei Zhang, Mulian Zheng, Xia Liu, Jian Ju, Changjiang Dong
Summary: This study addressed the issues of poor bonding and low forming strength in emulsified type cold-mix asphalt (CMA). Waterborne epoxy resin (WER) was used as an emulsifier, and samples of WER-emulsified CMA reinforced with polypropylene fiber were prepared in the laboratory. The optimal ratio of WER and curing agent, called WERC, was determined through various tests. The newly developed WER-emulsified CMA showed improved strength, temperature resistance, water stability, crack resistance, and bonding characteristics, making it suitable for rapid pavement pothole repair.
JOURNAL OF MATERIALS IN CIVIL ENGINEERING
(2022)
Article
Chemistry, Multidisciplinary
Abdulrahman Fahad Al Fuhaid, Md Arifuzzaman, Muhammad Aniq Gul
Summary: This study investigates the structural pavement design techniques for pavement distresses in Saudi Arabia's highway network. The research focuses on four regions in Saudi Arabia and uses the MEPDG software to calibrate and predict pavement design life. The results provide a base pavement design for designers to modify based on project requirements.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Mechanical
Marco Guerrieri, Giuseppe Parla
Summary: This article presents a robust and real-time low-cost automated method for detecting and measuring various distress types of road pavements using deep learning and YOLOv3 algorithm. The proposed technique achieves high accuracy and precision in detecting pavement distress and sheds light on new opportunities for using low-cost detection devices and artificial intelligence techniques in carrying out inspections of road pavements.
ENGINEERING FAILURE ANALYSIS
(2022)
Article
Construction & Building Technology
Sajad Ranjbar, Fereidoon Moghadas Nejad, Hamzeh Zakeri
Summary: The study aims to develop an image-based system for the comprehensive evaluation of pavement bleeding, including bleeding occurrence detection, bleeding region segmentation, and severity-based classification. The proposed system shows good performance in detection, segmentation, and severity-based classification parts, with average performance indices of 98%, 89%, and 93% respectively.
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING
(2022)
Article
Construction & Building Technology
Sepehr Ghafari, Mehrdad Ehsani, Fereidoon Moghadas Nejad
Summary: Fracture resistance curves (R-curves) provide a comprehensive tool for understanding crack propagation in engineering materials. This study conducted experiments on various HMA mixtures and used machine learning to develop prediction models for R-curves, finding that mixtures incorporating 20% crumb rubber showed desirable fracture resistance characteristics at low temperatures and could be successfully predicted.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Construction & Building Technology
Pouria Hajikarimi, Mehrdad Ehsani, Yassine El Haloui, Fateh Fakhari Tehrani, Joseph Absi, Fereidoon Moghadas Nejad
Summary: The main aim of this study is to develop prediction equations of asphalt mastic fractional model parameters. The results showed that the fractional viscoelastic model could accurately predict the viscoelastic behavior of the original and modified asphalt mastic using fewer parameters compared to the well-known generalized Maxwell model.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Engineering, Manufacturing
Akbar Danesh, Mehrdad Ehsani, Fereidoon Moghadas Nejad, Hamzeh Zakeri
Summary: Road accidents are a significant global issue with a high number of deaths. Accurately predicting fatal crashes, which constitute a minority class in comparison to non-fatal crashes, is a major challenge in machine learning algorithms. This study introduces data leveling methods based on metaheuristic optimization algorithms to balance the data and applies three machine learning algorithms for analysis. The results show that using these data leveling methods can improve the performance of crash prediction models, particularly in the SVM algorithm.
INTERNATIONAL JOURNAL OF CRASHWORTHINESS
(2022)
Article
Engineering, Mechanical
Sepehr Ghafari, Fereidoon Moghadas Nejad
Summary: J-Resistance curves are powerful tools for characterizing crack propagation trends in materials. The research found that reducing temperature increases crack blunting energy, while the incorporation of crumb rubber can enhance the blunting fraction of R-curves at different temperatures.
THEORETICAL AND APPLIED FRACTURE MECHANICS
(2022)
Article
Construction & Building Technology
Mehrdad Ehsani, Fereidoon Moghadas Nejad, Pouria Hajikarimi
Summary: Predicting faulting failure in concrete pavement design is achieved through artificial neural networks and random forest method, with an optimized model developed to identify key features influencing failure. Factors such as cumulative precipitation, concrete slab modulus, base thickness, and temperature play crucial roles in predicting faulting accurately. Sensitivity analysis helps determine optimal values for these key parameters.
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
Behrouz Mataei, Fereidoon Moghadas Nejad, Hamzeh Zakeri
Summary: This paper presents a 3D automatic device based on a cumulative imaging technique for measuring pavement texture. The device generates 3D point cloud models and extracts evaluation indices for assessing pavement texture, including a new method for evaluating texture in rainy conditions. Experimental tests demonstrate a high correlation between the results of this system and traditional sand patch tests.
Article
Construction & Building Technology
Alireza Houshangi Poshtmesari, Fereidoon Moghadas Nejad
Summary: This research evaluates the adhesion and moisture susceptibility of asphalt mixtures containing nanomaterials. The findings show that the nanomaterial-modified mixtures exhibit improved adhesion and moisture resistance.
JOURNAL OF MATERIALS IN CIVIL ENGINEERING
(2022)
Article
Chemistry, Multidisciplinary
Seyed Mohammad Sadegh Lajevardi, Fereidoon Moghadas Nejad, Mehdi Ravanshadnia
Summary: The durability of reinforced concrete is crucial for assessing long-term quality and structural performance. This research proposes an automated visualization approach for evaluating concrete durability by extracting quantitative indexes and conducting visual inspections, eliminating subjective interference and potential human errors.
APPLIED SCIENCES-BASEL
(2022)
Article
Construction & Building Technology
Mahdi Gholampour, Hossein Nazari, Koorosh Naderi, Fereidoon Moghadas Nejad
Summary: This study investigates the effects of three nanoparticles (silicon dioxide, titanium dioxide, and calcium carbonate) on the physical properties and rheological behavior of bitumen. The results show that the nanosilica has the most significant impact on the bitumen, while titanium dioxide and calcium carbonate are less effective. Additionally, the use of the 2S2P1D model and complex reinforcement coefficient R-M* proves to be practical for modeling and comparing the rheological properties of nanoparticle-modified binders.
PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-CONSTRUCTION MATERIALS
(2022)
Article
Green & Sustainable Science & Technology
Pooria Dashti, Sajad Ranjbar, Sepehr Ghafari, Amir Ramezani, Fereidoon Moghadas Nejad
Summary: In recent years, there has been increasing attention towards incorporating recycled materials into the production of eco-friendly construction materials. This research focuses on using waste tire constituents as binders in geopolymer materials, offering a sustainable alternative to conventional concrete. The study shows that incorporating recycled materials not only mitigates environmental impact but also improves the performance of the mixture.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Engineering, Mechanical
Pouria Hajikarimi, Mehrdad Ehsani, Fereidoon Moghadas Nejad, Amir H. Gandomi
Summary: The objective of this study is to create explicit prediction models for the complex shear modulus and phase angle of bitumen mastic using an evolutionary machine learning approach. The results showed that the hybrid machine learning approach can effectively develop precise, meaningful, and simple formulas for calculating these properties of the bitumen mastic.
JOURNAL OF ENGINEERING MECHANICS
(2023)
Article
Computer Science, Information Systems
Behrouz Mataei, Fereidoon Moghadas Nejad, Hamzeh Zakeri
Summary: The assessment of surface drainage is an important aspect in evaluating pavement condition, however it is often neglected in existing pavement management systems. This study proposes an image-based method for evaluating surface drainage capability of pavements, which consists of three steps: image preprocessing, feature extraction, and evaluation. Experimental results show that the proposed method is fast and efficient for assessing pavement surface drainage.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Civil
Soheil Ghyasvand, Ahamd Fahimifar, Fereidoon Moghadas Nejad
Summary: Safety is of utmost importance in underground railway transportation, and the stability of underground tunnels is crucial in tunneling engineering. Traditional railway tunnels in Iran are primarily supported by masonry structures or left unsupported in high quality rock masses. This study evaluates different reinforcement systems for supporting a 50-year-old tunnel in Iran named Keshvar. Through an optimization analysis, a systemic rock bolts and shotcrete protection method is found to be the most suitable for this type of tunnel in Iran.
GEOMECHANICS AND ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
Zhaleh Shams, Hamed Khani Sanij, Alireza Afshani, Ehsan Ramezani-Khansari, Fereidoon Moghadas Nejad, Martin Olazar
Summary: The significant increase in transportation and heavy vehicle traffic has led to decreased safety levels on congested freeway routes. Focus on fatigue and sleepiness, two main reasons for accidents, is crucial. It is important to address multiple factors in order to reduce accidents caused by fatigue and sleepiness.
JURNAL KEJURUTERAAN
(2022)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
