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Advanced Pavement Engineering: Design, Construction, and Performance

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (30 October 2024) | Viewed by 19228

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Special Issue Editors

Dr. Derun Zhang

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Guest Editor

School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: smart pavement infrastructure; intelligent paving and compaction; non-destructive testing and evaluation; pavement condition assessment; pavement distress detection

Dr. Chao Xing

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Guest Editor

School of Transportation Science and Engieering, Harbin Institute of Technology, Harbin 150090, China
Interests: multiscale characterization; digital design; intelligent monitoring
Special Issues, Collections and Topics in MDPI journals

Dr. Quan Lv

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Guest Editor

Department of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: adhesion and healing behavior between the asphalt and aggregate; properties of various modified asphalt and emulsified asphalt
Special Issues, Collections and Topics in MDPI journals

Dr. Ju Huyan

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Guest Editor

School of Transportation, Southeast University, Nanjing 211189, China
Interests: image processing; computer vision; machine learning; deep learning and related advanced technologies for high-efficiency damage; FOD detections; innovative and reliable pavement; AI facilitated infrastructure condition assessment and decision-making; intelligent and efficient pavement performance monitoring and management systems

Dr. Song Liu

E-Mail Website
Guest Editor

College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
Interests: functional pavement structure and material; asphalt railway track design and optimization; biomass application in asphalt pavement

Special Issue Information

Dear Colleagues,

Developing durable and resilient pavement infrastructures is critical for current economic growth and environmental sustainability. With the rapid increment of road mileages across the world, the development of new roads in the world has almost reached saturation. However, most of the in-service roads currently face problems centred around performance degradation, especially for initially poorly constructed roads. Meanwhile, the high consumption of non-renewable natural resources used in transport infrastructure calls for reasonable usage. Therefore, effective advanced pavement condition assessments and M&R strategies are urgently required and prioritized in pavement engineering in order to extend pavement lifetimes. Moreover, due to the coupled behaviors of traffic and environmental conditions, the accurate prediction of pavement service life becomes increasingly complicated and requires a deep knowledge from all aspects of pavement design, construction, and performance.

The aim of this Special Issue is to provide references and insights for scholars and researchers in the field of pavement engineering. We would like to invite you to contribute original research articles as well as review articles that discuss recent advancements regarding the design, construction, and performance of pavement, including (but not limited to) the following themes:

Advanced technologies for development of functional and sustainable pavement structures and materials;
Innovative and reliable pavement design and performance modelling methods;
Cost-effective and environmentally friendly pavement construction technologies;
Intelligent and efficient pavement performance monitoring and management systems.

If you are interested in this topic and would like to share your work with us, please do not hesitate to contact us.

Dr. Derun Zhang
Dr. Chao Xing
Dr. Quan Lv
Dr. Ju Huyan
Dr. Song Liu
Guest Editors

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Keywords

pavement engineering
pavement structures and materials
design methods
construction technologies
performance monitoring and management systems
durability
resilience
sustainability

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Published Papers (9 papers)

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Research

17 pages, 3342 KiB

Open AccessArticle

Sustainable Pavement Management: Harnessing Advanced Machine Learning for Enhanced Road Maintenance

by Kshitij Ijari and Carlos D. Paternina-Arboleda

Appl. Sci. 2024, 14(15), 6640; https://doi.org/10.3390/app14156640 - 30 Jul 2024

Cited by 1 | Viewed by 1009

Abstract

In this study, we introduce an advanced system for sustainable pavement management that leverages cutting-edge machine learning and computer vision techniques to detect and classify pavement damage. By utilizing models such as EfficientNetB3, ResNet18, and ResNet50, we develop robust classifiers capable of accurately identifying various types of pavement distress. To further enhance our dataset, we employ a Swin Transformer-based Generative Adversarial Network (GAN) to synthetically generate images of pavement cracks, thereby augmenting the training data. Our approach aims to improve the efficiency and accuracy of pavement damage assessment, contributing to more effective and sustainable road maintenance practices. This research aligns with the sustainable development goals by fostering innovative methods that extend the lifespan of infrastructure, reducing the need for resource-intensive repairs, and promoting the longevity and reliability of road networks. The outcomes of this study are discussed in terms of their potential impact on infrastructure safety and sustainability, with suggestions for future research directions. This study demonstrates how integrating advanced machine learning techniques into pavement management systems can enhance decision-making, optimize resource allocation, and improve the sustainability of infrastructure maintenance practices. By leveraging big data and sophisticated algorithms, stakeholders can proactively address pavement deterioration, extend asset lifespan, and optimize maintenance efforts based on real-time data-driven insights. Full article

(This article belongs to the Special Issue Advanced Pavement Engineering: Design, Construction, and Performance)

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14 pages, 6706 KiB

Open AccessArticle

Finite Element Analysis of Geogrid-Incorporated Flexible Pavement with Soft Subgrade

by Sagar Chhetri and Plaban Deb

Appl. Sci. 2024, 14(13), 5798; https://doi.org/10.3390/app14135798 - 3 Jul 2024

Viewed by 1132

Abstract

Improving the durability of flexible pavements and constructing new roads on weak soil foundations present significant challenges, prompting designers to explore alternative methods to prolong pavement lifespan. Geosynthetics have emerged as a promising solution for soil stabilization, with various materials developed for this purpose. The current study concentrates on using the finite element (FE) method to examine the effectiveness of geogrid-incorporated flexible pavements on soft soil substrates. A three-dimensional layered pavement is constructed with an FE model, incorporating subgrade layers of varying strengths based on their California bearing ratio (CBR) values, with a geogrid layer implemented to enhance subgrade stability. Additionally, attention is also given to investigating the effect of base course thickness. The findings reveal that the geogrid layer primarily influences the formation of plastic strains in the subgrade rather than resilient strains, effectively reducing vertical compressive strain by approximately 40%. With increasing CBR values, there is a reduction in vertical strain, although the influence zone extends up to a depth of 300 mm within the subgrade. At the surface of the subgrade, vertical strain decreases by around 17%, 39%, and 49% as the CBR values increase from 1% to 3%, 5%, and 8%, respectively. Full article

(This article belongs to the Special Issue Advanced Pavement Engineering: Design, Construction, and Performance)

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23 pages, 40076 KiB

Open AccessArticle

A Pavement Crack Detection and Evaluation Framework for a UAV Inspection System Based on Deep Learning

by Xinbao Chen, Chang Liu, Long Chen, Xiaodong Zhu, Yaohui Zhang and Chenxi Wang

Appl. Sci. 2024, 14(3), 1157; https://doi.org/10.3390/app14031157 - 30 Jan 2024

Cited by 6 | Viewed by 2290

Abstract

Existing studies often lack a systematic solution for an Unmanned Aerial Vehicles (UAV) inspection system, which hinders their widespread application in crack detection. To enhance its substantial practicality, this study proposes a formal and systematic framework for UAV inspection systems, specifically designed for automatic crack detection and pavement distress evaluation. The framework integrates UAV data acquisition, deep-learning-based crack identification, and road damage assessment in a comprehensive and orderly manner. Firstly, a flight control strategy is presented, and road crack data are collected using DJI Mini 2 UAV imagery, establishing high-quality UAV crack image datasets with ground truth information. Secondly, a validation and comparison study is conducted to enhance the automatic crack detection capability and provide an appropriate deployment scheme for UAV inspection systems. This study develops automatic crack detection models based on mainstream deep learning algorithms (namely, Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s) in urban road scenarios. The results demonstrate that the Faster-RCNN algorithm achieves the highest accuracy and is suitable for the online data collection of UAV and offline inspection at work stations. Meanwhile, the YOLO models, while slightly lower in accuracy, are the fastest algorithms and are suitable for the lightweight deployment of UAV with online collection and real-time inspection. Quantitative measurement methods for road cracks are presented to assess road damage, which will enhance the application of UAV inspection systems and provide factual evidence for the maintenance decisions made by road authorities. Full article

(This article belongs to the Special Issue Advanced Pavement Engineering: Design, Construction, and Performance)

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24 pages, 7072 KiB

Open AccessArticle

Dynamic Response Analysis of JPCP with Different Roughness Levels under Moving Axle Load Using a Numerical Methodology

by Chuang Yan and Ya Wei

Appl. Sci. 2023, 13(19), 11046; https://doi.org/10.3390/app131911046 - 7 Oct 2023

Viewed by 4336

Abstract

In-service Portland cement concrete (PCC) pavements are subject to repeated dynamic loads from moving vehicles; thus, the actual stress generated in a PCC pavement may significantly differ from the static stress, which is normally used in the design and evaluation of pavement performance. Calculating the stress in PCC pavements under moving vehicle loads is of importance to assess their actual service condition, particularly for pavements with different surface roughness levels as the deteriorated roughness might cause large stress in PCC pavement subject to dynamic loads. In this paper, a method is proposed to compute the dynamic response in terms of loads and stresses generated in jointed plain concrete pavements (JPCPs) under a moving axle load, considering the effects of the pavement surface roughness, the vehicle parameters (including vehicle speeds and axle weights), and the pavement structure parameters (including thickness and elastic modulus of different layers and the existence of dowel bars). The dynamic axle load is firstly generated based on the quarter-car model, running through three successive slabs of which the surface roughness is determined by the power spectral density method, and the critical locations in slabs where the largest tensile stresses occur are identified. The combined effects of various pavement surface roughness levels, vehicle speeds, axle weights, and pavement structure parameters are evaluated in terms of the stress and the dynamic factor defined as the ratio of the tensile stress under dynamic load to the tensile stress under static load. For the roughness level D, the tensile stress can reach a maximum value of 3.13 MPa, and the dynamic factor can reach a maximum value of 2.46, which is much larger than the dynamic factor of 1.15 or 1.2 currently used in design guidebooks. Increasing the thicknesses of pavement slab or the subbase layer is an effective way to reduce the tensile stress in JPCP, while increasing the thickness of base layer is not effective. The results of this study can benefit future pavement design and pavement performance evaluation by providing the actual stress and the useful dynamic factor values for various conditions of field pavements. Moreover, preventive maintenance, particularly the improvement of pavement surface roughness, can be planned by referring to the results of this study, to avoid large tensile stress generated in JPCPs. Full article

(This article belongs to the Special Issue Advanced Pavement Engineering: Design, Construction, and Performance)

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29 pages, 5044 KiB

Open AccessArticle

Design and Low-Temperature Performance Evaluation of High-Modulus Co-Modified Asphalt Mixes with Rock Asphalt/Rubber Powder

by Lianfang Wang, Lijun Sun and Quan Lv

Appl. Sci. 2023, 13(14), 8075; https://doi.org/10.3390/app13148075 - 11 Jul 2023

Viewed by 1323

Abstract

High-modulus asphalt mixes are effective means to solve rutting problems, but they perform poorly at low temperatures. This study aims to enhance the modulus and low-temperature properties of mixes. Firstly, composite-modified asphalts and mixes were prepared by incorporating rubber powder and rock asphalt. Secondly, their mechanical and viscoelastic properties were investigated to determine the appropriate mass ratios of rubber powder and rock asphalt in asphalt to be 20% and 6%, respectively. The results show that both rock asphalt and rubber powder can enhance the softening point and viscosity of basic asphalt while reducing penetration. Furthermore, their combination significantly improves the high-temperature performance of the material. It is noteworthy that the rubber powder also improves the weakening of rock asphalt for mixtures at low temperatures. Finally, this study employs dynamic and static modulus tests, rutting tests, and beam bending tests to clarify the road properties of composite-modified asphalt mixes. The results indicate that mixes have high-modulus and water damage resistance while considering acceptable low-temperature performance. This paper not only enhances the adaptability of high-modulus asphalt in different environments but also expands its application range. Full article

(This article belongs to the Special Issue Advanced Pavement Engineering: Design, Construction, and Performance)

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16 pages, 3297 KiB

Open AccessArticle

Automated Bridgehead Settlement Detection on the Non-Staggered-Step Structures Based on Settlement Point Ratio Model

by Hong Lang, Yuan Peng, Zheng Zou, Shengxue Zhu, Zhen Chen and Meng Zhang

Appl. Sci. 2023, 13(13), 7888; https://doi.org/10.3390/app13137888 - 5 Jul 2023

Cited by 1 | Viewed by 1030

Abstract

The bridgehead settlement problem continues to be one of the most chronic issues affecting long-term bridge performance. In addition, the magnitude of non-staggered-step settlement across the bridge approach transition has not been quantified. Non-contact measurement is considered an alternative to manual inspection, enabling automated damage evaluation for structural maintenance. This paper proposes an inexpensive automatic system using an inertial navigation sensor and a line scanning camera to evaluate the non-staggered-step bridgehead settlement with acceptable accuracy. By analyzing road longitudinal slope data, driving distance, and pavement images, this paper established a calculation model and algorithm of non-staggered-step bridgehead settlement, in which case, a new calculation index named the settlement point ratio (SPR) was proposed. Moreover, the effect of the vehicular detection system and the distance gradient tested at three speeds were measured. The results illustrate that the system has a good performance in longitudinal slope data with an absolute error of less than 1.5%. In addition, 31 bridges in China, Ningbo city, were selected. Combined with the test data, 50 groups of SPR were output using the established model and algorithm. By validating the system’s output with the standard measurement method, correlation, and regression analysis were carried out in order to verify the SPR model’s reliability. The correlation coefficient is 0.934, and the determination coefficient of the regression model is 0.872, which confirms its capability for accurate data collection and settlement measurement. Therefore, the proposed method is scientific and reasonable for detecting and quantifying non-staggered-step bridgehead settlement, effectively completing the research blank of bridgehead settlement detection. Full article

(This article belongs to the Special Issue Advanced Pavement Engineering: Design, Construction, and Performance)

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16 pages, 8100 KiB

Open AccessArticle

Study of Pavement Macro- and Micro-Texture Evolution Law during Compaction Using 3D Laser Scanning Technology

by Yuchao Lin, Chenyang Dong, Difei Wu, Shengchuan Jiang, Hui Xiang and Zihang Weng

Appl. Sci. 2023, 13(9), 5736; https://doi.org/10.3390/app13095736 - 6 May 2023

Cited by 5 | Viewed by 2587

Abstract

The pavement macro-texture and micro-texture are crucial factors for evaluating pavement performance as they have a significant correlation with friction, water film formation, and driving safety. During pavement construction, the macro-texture and micro-texture are significantly related to compaction operations. However, the current approach for evaluating pavement texture still relies on post-construction acceptance, with few considerations on the evolution patterns of pavement texture during the compaction process. Therefore, this study aimed to investigate the texture evolution law during compaction by implementing a laboratory compaction method. High-precision texture data from various asphalt mixtures were collected using 3D laser scanning during laboratory compaction. Macro-texture and micro-texture parameters were used to evaluate surface texture. Nineteen traditional geometric parameters were calculated at the macro-level to analyze macro-texture characteristics, while a 2D wavelet transform approach was applied at the micro-level to extract micro-texture, and the energy of each level and relative energy were calculated as indicators. This study analyzed the evolution law of parameters and found that certain parameters tend to converge. Moreover, geometric parameters and energy at lower levels of the samples could be utilized as supervising factors to regulate the compaction process. Full article

(This article belongs to the Special Issue Advanced Pavement Engineering: Design, Construction, and Performance)

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15 pages, 10353 KiB

Open AccessArticle

Dynamic Responses of Semi-Flexible Pavements Used for the Autonomous Rail Rapid Transit

by Biao Pan, Hongjian Zhang, Song Liu, Minghui Gong and Jun Yang

Appl. Sci. 2023, 13(6), 3673; https://doi.org/10.3390/app13063673 - 14 Mar 2023

Cited by 5 | Viewed by 1988

Abstract

The application of a semi-flexible pavement (SFP) is an effective solution to solve the rutting problems of the autonomous rail rapid transit (ART) system. The service environment of an ART pavement is significantly different from that of the conventional pavement due to the large axle load and high tire pressure of ART vehicles. A test section was constructed in the Zhuzhou ART system and a tire–pavement coupling FE model was built to explore the distribution features of the dynamic responses as well as to optimize the material and structural design. The tire–pavement coupling model was firstly verified by the field test data and then, utilizing the validated model, the parameter study was performed to analyze the influence of the vehicle operating state and pavement conditions. The simulation results show that the transverse tensile strain at the bottom of the SFP layer is dominant for the fatigue cracking of the pavement. Properly reducing the tire pressure can effectively improve the tensile environment at the bottom of the SFP layer. The action of the braking force may cause significant longitudinal tensile strains at the surface of the SFP layer and lead to transverse cracking of the semi-flexible ART pavement. The interlayer bonding between the SFP layer and the asphalt layer has significant influence on the amplitude and distribution of tensile stress at the bottom of the SFP layer. Moreover, to optimize the tensile environment of the semi-flexible ART pavement, the thickness of the SFP layer and the asphalt concrete layer cannot differ too much under the premise of meeting the requirements of rutting resistance performance. Full article

(This article belongs to the Special Issue Advanced Pavement Engineering: Design, Construction, and Performance)

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20 pages, 29581 KiB

Open AccessArticle

Study on Mechanical Characteristics of BRT Asphalt Pavement Structures Based on Temperature Field and Traffic Load

by Yu Wu and Yubi Zhao

Appl. Sci. 2023, 13(6), 3423; https://doi.org/10.3390/app13063423 - 8 Mar 2023

Cited by 4 | Viewed by 1524

Abstract

To reveal the mechanical characteristics of BRT asphalt pavement structures under the combined effects of the temperature field and load of buses, a series of finite element analysis models were established in the software application ABAQUS to simulate the Lanzhou BRT asphalt pavement project. The actual BRT road temperature field in summer and loads of buses at different speeds were introduced in the model with user subroutines before conducting a sequentially coupled thermal-mechanical analysis. The results indicated that the BRT asphalt pavement structure readily experienced permanent deformation, mainly comprising unstable rutting during the high-temperature season, and the possibility of cracking was higher for the subbase bottom than for the base. Temperature imposed a greater influence than BRT vehicle frequency. To delay fatigue cracking of the base and subbase and the shear failure of asphalt pavement structures, BRT operating speed should be controlled within 30–40 km/h. In actual BRT asphalt pavement engineering, special attention should be given to the deformation resistance of the intermediate surface layer. Full article

(This article belongs to the Special Issue Advanced Pavement Engineering: Design, Construction, and Performance)

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