Article
Engineering, Civil
B. Ravi Kiran, Ibrahim Sobh, Victor Talpaert, Patrick Mannion, Ahmad A. Al Sallab, Senthil Yogamani, Patrick Perez
Summary: This paper summarizes deep reinforcement learning algorithms, provides a taxonomy of automated driving tasks, discusses key computational challenges in real world deployment of autonomous driving agents, and explores adjacent domains as well as the role of simulators in training agents.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Wu Deng, Junjie Xu, Huimin Zhao, Yingjie Song
Summary: The paper introduces a three-objective gate allocation model to optimize passenger walking distances, balanced idle time, and efficient use of gates. An IPOQEA algorithm is proposed to efficiently solve the model, with validation on the effectiveness at Baiyun Airport.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Hongtian Chen, Bin Jiang, Steven X. Ding, Biao Huang
Summary: This paper provides a systematic review and categorization of data-driven FDD methods for traction systems in high-speed trains. It analyzes the challenges in implementing FDD and proposes several promising solutions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Review
Engineering, Civil
Sajjad Mozaffari, Omar Y. Al-Jarrah, Mehrdad Dianati, Paul Jennings, Alexandros Mouzakitis
Summary: This article provides a comprehensive review of deep learning-based approaches for vehicle behavior prediction. It discusses the challenges and issues in behavior prediction and categorizes and reviews the most recent solutions based on input representation, output type, and prediction method. The article also evaluates the performance of several solutions and outlines potential future research directions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Ammar Haydari, Yasin Yilmaz
Summary: Latest technological improvements have enhanced the quality of transportation. The emergence of new data-driven approaches has opened up new research directions for control-based systems in various domains, including transportation, robotics, IoT, and power systems. This paper presents a survey of traffic control applications based on deep reinforcement learning (RL). It extensively discusses different problem formulations, RL parameters, and simulation environments for traffic signal control (TSC) applications. The survey also covers autonomous driving applications studied with deep RL models, categorizing them based on application types, control models, and algorithms studied. The paper concludes with a discussion on challenges and open questions in deep RL-based transportation applications.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Seyed Mojtaba Marvasti-Zadeh, Li Cheng, Hossein Ghanei-Yakhdan, Shohreh Kasaei
Summary: This survey systematically investigates current deep learning-based visual tracking methods, benchmark datasets, and evaluation metrics, while extensively evaluating leading visual tracking methods.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Construction & Building Technology
Yingxia Li, Shibin Ma, Guang Chen, Shuai Wang
Summary: This study investigates the effects of waste foundry sand (WFS) on the performance of cement-stabilised macadam (CSM). The results show that the addition of WFS reduces the mixture properties, but can be improved with increasing curing age and cement dosage. The optimum cement dosage is 4%, and the optimum WFS replacement level for natural fine aggregate is 15%.
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING
(2023)
Article
Engineering, Civil
Szilard Aradi
Summary: Academic research in the field of autonomous vehicles has gained popularity in recent years, covering various topics such as sensor technologies, communication, safety, decision making, and control. Artificial Intelligence and Machine Learning methods have become integral parts of this research. Motion planning, with a focus on strategic decision-making, trajectory planning, and control, has also been studied. This article specifically explores Deep Reinforcement Learning (DRL) as a field within Machine Learning. The paper provides insights into hierarchical motion planning and the basics of DRL, including environment modeling, state representation, perception models, reward mechanisms, and neural network implementation. It also discusses vehicle models, simulation possibilities, and computational requirements. The paper surveys state-of-the-art solutions, categorized by different tasks and levels of autonomous driving, such as car-following, lane-keeping, trajectory following, merging, and driving in dense traffic. Lastly, it raises open questions and future challenges.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Yongze Song, Peng Wu, Qindong Li, Yuchen Liu, Lalinda Karunaratne
Summary: Strategic maintenance is crucial for sustainable road infrastructure development. Accurate estimation of road maintenance effects can support the assessment of maintenance strategies and reasonable allocation of budgets and resources. The study developed a dynamic trade-off model (DTOM) to quantify the impacts of different factors, and found that 12 years of maintenance activities at the network level have effectively reduced roughness deterioration and improved road performance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Zhihan Lv, Yuxi Li, Hailin Feng, Haibin Lv
Summary: The study aims to enhance the security performance of digital twins in the Cooperative Intelligent Transportation System in a deep learning environment. By combining Convolutional Neural Network with Support Vector Regression, a model is constructed and analyzed through simulation experiments. Results show that the proposed algorithm has significant advantages in security performance and data transmission speed.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Jinchao Chen, Chenglie Du, Ying Zhang, Pengcheng Han, Wei Wei
Summary: Unmanned aerial vehicles (UAVs) are widely utilized in civilian and military applications for their high autonomy and strong adaptability. This paper addresses the coverage path planning problem of autonomous heterogeneous UAVs on a bounded number of regions by proposing an exact formulation based on mixed integer linear programming and a clustering-based algorithm inspired from density-based clustering methods to achieve optimal flight paths and efficient coverage tasks. Experiments demonstrating the efficiency and effectiveness of the proposed approach with randomly generated regions are conducted.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Mohamad W. Zaitoun, Abdelbaki Chikh, Abdelouahed Tounsi, Mohammed A. Al-Osta, Alfarabi Sharif, Salah U. Al-Dulaijan, Mesfer M. Al-Zahrani
Summary: This study investigates the buckling response of functionally graded sandwich plates on a viscoelastic foundation under hygrothermal conditions using higher-order shear deformation theory. An accurate solution is developed and a parametric study is conducted on the effect of damping coefficient, aspect ratio, moisture condition, power-law index, and temperature variation on the buckling temperature.
THIN-WALLED STRUCTURES
(2022)
Review
Engineering, Civil
Yaodong Cui, Ren Chen, Wenbo Chu, Long Chen, Daxin Tian, Ying Li, Dongpu Cao
Summary: The development of autonomous vehicles has been rapid in recent years, yet achieving full autonomy poses challenges due to the complex and dynamic driving environments. The fusion of camera and LiDAR sensors using deep learning is an emerging research theme. Despite the lack of critical reviews on deep-learning-based camera-LiDAR fusion methods, recent research has focused on leveraging image and point cloud data processing for improved environmental perception and object detection.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Jiexia Ye, Juanjuan Zhao, Kejiang Ye, Chengzhong Xu
Summary: This article surveys graph-based deep learning architectures in the traffic domain, providing guidelines for problem formulation and graph construction, discussing shared deep learning techniques, and presenting graph neural network solutions for traffic challenges.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Construction & Building Technology
Wei Jiang, Dongdong Yuan, Jinhuan Shan, Wanli Ye, Hehe Lu, Aimin Sha
Summary: The study found that in Porous ultra-thin asphalt overlay (PUAO), as the air voids increased, the high-temperature rutting dynamic stability decreased, the tensile strength ratio first decreased and then increased, and the acoustic absorption coefficient gradually increased, but the peak frequency value shifted. The PUAO/AC-13 double-layer specimens showed better performance in dynamic stability and low-temperature bending strength compared to the PUAO single-layer specimens, but had a smaller permeability coefficient. The International Friction Index results showed that PUAO material had excellent skid resistance performance.
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING
(2022)
Article
Construction & Building Technology
Mohamed Amin, Abdullah M. Zeyad, Bassam A. Tayeh, Ibrahim Saad Agwa
Summary: This study introduces a new material, ferrosilicon slag, as a partial substitute for cement in ultra high-performance concrete (UHPC), with microparticles having a surface area of 12,850 (cm2/gm). Results show that increasing the replacement rates of ferrosilicon slag and silica fume by up to 25% can significantly enhance the compressive strength and transport properties of UHPC. This material also has an impact on the fresh concrete properties and mechanical properties of UHPC.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Review
Construction & Building Technology
Shaker M. A. Qaidi, Bassam A. Tayeh, Haytham F. Isleem, Afonso R. G. de Azevedo, Hemn Unis Ahmed, Wael Emad
Summary: This paper provides a systematic review of the use of waste red mud and slag for the production of red mud-slag geopolymer. It discusses the economic and environmental impacts, physical and chemical properties, and potential applications of red mud. The study also presents recent advancements in the usage of red mud and slag for geopolymer production.
CASE STUDIES IN CONSTRUCTION MATERIALS
(2022)
Article
Construction & Building Technology
Ayaz Ahmad, Waqas Ahmad, Fahid Aslam, Panuwat Joyklad
Summary: This study utilized machine learning algorithms to predict the compressive strength of fly ash-based geopolymer concrete, with the bagging model showing superior performance in result prediction. Sensitivity analysis was conducted to determine the contribution of each parameter towards result prediction.
CASE STUDIES IN CONSTRUCTION MATERIALS
(2022)
Review
Engineering, Civil
Huu-Tai Thai
Summary: This paper provides a comprehensive review on the applications of machine learning in structural engineering, focusing on basic concepts, tools, and datasets. The research covers various aspects of structural analysis, health monitoring, and fire resistance. The paper summarizes the findings and discusses challenges and future recommendations.
Article
Engineering, Civil
Yunchao Tang, Zhaofeng Huang, Zheng Chen, Mingyou Chen, Hao Zhou, Hexin Zhang, Junbo Sun
Summary: State-of-the-art machine-vision systems have limitations in crack width measurements. This study proposes a new crack backbone refinement algorithm and width-measurement scheme to improve measurement accuracy by simplifying crack images and defining accurate sample points.
ENGINEERING STRUCTURES
(2023)
