Service Safety and Green Maintenance Technology for Road Infrastructure in Complex Environments

Topic Information

Dear Colleagues,

Road infrastructure worldwide faces unprecedented challenges from intensifying climate change, increasing extreme weather events, and the pressing imperative of sustainable development. Complex environmental stressors—such as extreme temperature, heavy rainfall, and snowfall—severely threaten the durability and service safety of pavements. Concurrently, the global commitment to carbon neutrality demands a fundamental shift towards greener, more resilient, and intelligent infrastructure systems throughout their entire life cycle.

Despite significant improvement in materials and structures, infrastructure often suffers from premature deterioration, short service life, and high maintenance burdens in complex environments. Furthermore, traditional maintenance approaches struggle with the predictive assessment of damage and the efficient implementation of low-carbon solutions, highlighting an urgent need for technological integration and innovation.

This Topic aims to compile cutting-edge research and foster interdisciplinary dialogue to address these critical gaps. We invite original and review articles focusing on, but not limited to, the following topics: (1) advanced and resilient pavement materials; (2) green maintenance and rehabilitation technologies; (3) intelligent monitoring and safety assurance methodology; (4) resource efficiency improvement and life cycle assessment; and (5) integrated resilience enhancement and lifespan extension measurements. By exploring these themes, this Topic seeks to advance the scientific foundation for the next generation of safe, sustainable, and intelligent road infrastructure.

Dr. Fucheng Guo
Dr. Bochao Zhou
Dr. Wentong Wang
Dr. Dongdong Yuan
Dr. Di Wang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Buildings buildings	3.1	4.4	2011	15.1 Days	CHF 2600	Submit
CivilEng civileng	2.0	4.0	2020	21.7 Days	CHF 1400	Submit
Coatings coatings	2.8	5.4	2011	13 Days	CHF 2600	Submit
Construction Materials constrmater	-	3.1	2021	20.9 Days	CHF 1200	Submit
Infrastructures infrastructures	2.9	6.0	2016	18.3 Days	CHF 1800	Submit
Materials materials	3.2	6.4	2008	15.5 Days	CHF 2600	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (4 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Applied Sciences Buildings CivilEng Coatings Construction Materials Infrastructures Materials

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

23 pages, 3263 KB  

Open AccessArticle

Grading Design and Performance Evaluation of Porous Asphalt Mixture: A Synergistic Optimization of Pavement Performance and Sound Absorption

by Shiqi Xie, Peng Lu, Wenke Yan, Shengxu Wang, Yi Lu, Jinpeng Zhu and Mulian Zheng

Infrastructures 2026, 11(3), 108; https://doi.org/10.3390/infrastructures11030108 - 21 Mar 2026

Viewed by 213

Abstract

To address the current absence of targeted gradation design for porous asphalt pavements both domestically and internationally, this study employs the Coarse Aggregate Void Filling (CAVF) method to design the gradation of porous asphalt mixtures. Marshall stability tests, rutting tests, and scattering tests [...] Read more.

To address the current absence of targeted gradation design for porous asphalt pavements both domestically and internationally, this study employs the Coarse Aggregate Void Filling (CAVF) method to design the gradation of porous asphalt mixtures. Marshall stability tests, rutting tests, and scattering tests were conducted to investigate the relationship between coarse aggregate proportions and the structural stability of the mixture skeleton. An orthogonal experimental design was further utilized to examine the influence of three levels of fine aggregate gradation on the acoustic absorption characteristics of the mixture, and to analyze the effects of aggregate gradation on the primary pore diameter, connected pore diameter, and connected pore length. The results indicate that the coarse aggregate gradation predominantly governs the skeleton strength and overall pavement performance of the mixture, whereas the fine aggregate gradation exhibits significant effects on the interconnected void ratio, pore structure, and sound absorption performance. The optimal roughness range of coarse aggregates in porous asphalt mixtures is determined to be 0.46–0.52. The proportion of 0.6–1.18 mm aggregates has a pronounced influence on the primary pore diameter, connected pore diameter, and connected pore length. By integrating the design considerations for both coarse and fine aggregate gradations, a recommended gradation range for porous asphalt mixtures is proposed that achieves a balance between pavement performance and sound absorption/noise-reduction effectiveness. Full article

(This article belongs to the Topic Service Safety and Green Maintenance Technology for Road Infrastructure in Complex Environments)

►▼ Show Figures

Figure 1

27 pages, 7096 KB  

Open AccessArticle

From Simulation to Reality: GAN-Based Transformation of Pavement Defect Images for YOLO Detection

by Jiangang Yang, Shukai Yu, Yuquan Yao, Shiji Cao and Xiaojuan Ai

Appl. Sci. 2026, 16(6), 2978; https://doi.org/10.3390/app16062978 - 19 Mar 2026

Viewed by 262

Abstract

The application of three-dimensional ground-penetrating radar (3D GPR) for intelligent pavement defect analysis is often constrained by the limited availability of labeled samples. To address this challenge, this study employed Ground Penetrating Radar Maxwell (GprMax) to simulate typical pavement defects, including cracks, loose [...] Read more.

The application of three-dimensional ground-penetrating radar (3D GPR) for intelligent pavement defect analysis is often constrained by the limited availability of labeled samples. To address this challenge, this study employed Ground Penetrating Radar Maxwell (GprMax) to simulate typical pavement defects, including cracks, loose materials, and interlayer debonding. A Cycle-Consistent Generative Adversarial Network (Cycle-GAN) was then introduced to perform style transfer on the simulated images, thereby reducing the domain gap between simulated and real radar images. Furthermore, four You Only Look Once (YOLO) models—YOLO version 5, YOLOX, YOLO version 7, and YOLO version 8—were systematically compared using real datasets to identify the best-performing model, which was subsequently used to evaluate the effect of different proportions of synthetic data on detection performance. The results demonstrated that the moderate inclusion of synthetic data improved the recognition accuracy of loose defects (from 76.7% to 78.9%), whereas its impact on crack and debonding detection was negative. Moreover, excessive reliance on synthetic data led to overfitting, thereby reducing the model’s generalization capability. Among the four models, YOLOv7 achieved the best overall performance, with a mean Average Precision (mAP) of 83.4% and a crack detection rate of 88.2%. This study thus provides a feasible technical pathway and model selection reference for automated GPR-based pavement defect identification, offering practical value for efficient and accurate road maintenance inspections. Full article

(This article belongs to the Topic Service Safety and Green Maintenance Technology for Road Infrastructure in Complex Environments)

►▼ Show Figures

Figure 1

17 pages, 7431 KB  

Open AccessArticle

Mechanical Properties and Constitutive Model of Rapid-Curing Epoxy Resin Concrete Under Different Temperature Conditions

by Nannan Sun, Chuandong Shen, Jingwen Shen and Yuzhu Wang

Materials 2026, 19(5), 996; https://doi.org/10.3390/ma19050996 - 5 Mar 2026

Viewed by 384

Abstract

Recently, epoxy resin concrete (ERC) has shown significant potential in rapid repair applications, such as bridge expansion joints, owing to its early strength gain, rapid hardening, excellent adhesion, and durability. Based on the background of rapid repair scenarios for small- and medium-span bridges, [...] Read more.

Recently, epoxy resin concrete (ERC) has shown significant potential in rapid repair applications, such as bridge expansion joints, owing to its early strength gain, rapid hardening, excellent adhesion, and durability. Based on the background of rapid repair scenarios for small- and medium-span bridges, this study designed a mix proportion of ERC. A systematic investigation was conducted on its mechanical properties and constitutive model under various curing temperatures (5 °C, 20 °C, and 35 °C) and ages. Experimental results indicate that the designed ERC cures within 2 to 6 h and achieves a compressive strength of 15 MPa at 1 day, meeting the requirement for early traffic reopening. Both material strength and elastic modulus increase significantly with age, reaching a compressive elastic modulus of 16 GPa at 90 days. Based on the measured uniaxial compressive and tensile stress–strain data, a temperature-dependent constitutive model was established. The fitting parameters exhibit a quadratic functional relationship with curing temperature. The model demonstrates high fitting accuracy under all tested conditions (R² ≥ 0.9293). This study provides a theoretical basis and data support for the application and numerical simulation of ERC in bridge engineering. Full article

(This article belongs to the Topic Service Safety and Green Maintenance Technology for Road Infrastructure in Complex Environments)

►▼ Show Figures

Figure 1

19 pages, 3761 KB  

Open AccessArticle

Adhesion Mechanism and Quantitative Evaluation of Bio-Based and Petroleum-Based Oil-Modified Asphalt

by Wei Zhang, Xiao Ye, Mingwei Liu, Yongchang Cui, Lei Zhang and Haoan Wang

Coatings 2026, 16(2), 253; https://doi.org/10.3390/coatings16020253 - 16 Feb 2026

Viewed by 327

Abstract

The utilization of waste and renewable oils as asphalt modifiers is a crucial strategy for achieving sustainable development in pavement engineering. However, the different physicochemical effects exerted by oil sources (bio-based versus petroleum-based) on the asphalt–aggregate interface remain insufficiently understood. This study aims [...] Read more.

The utilization of waste and renewable oils as asphalt modifiers is a crucial strategy for achieving sustainable development in pavement engineering. However, the different physicochemical effects exerted by oil sources (bio-based versus petroleum-based) on the asphalt–aggregate interface remain insufficiently understood. This study aims to elucidate the influence mechanism of two bio-based oils and two petroleum-based oils on asphalt adhesion and the pavement performance of mixtures. A quantitative evaluation method combining the boiling test with digital image processing (DIP) technology was developed to assess the anti-stripping performance of modified asphalt on different lithological aggregates (acidic granite and alkaline limestone). Additionally, Fourier transform infrared spectroscopy (FTIR) was employed to reveal the chemical evolution of the modified asphalt. The results indicated that, although all oil-based modifiers demonstrated excellent compatibility and storage stability with the base asphalt (segregation ratio < 5%), their adhesion properties were significantly influenced by aggregate lithology. The key finding was that, compared to petroleum-based oils, bio-based oils exhibited superior adhesion performance on acidic granite surfaces, markedly mitigating moisture-induced stripping. FTIR analysis confirmed that this enhancement was attributable to the aromatic and carbonyl functional groups introduced by bio-based oils, which effectively promoted the interfacial bonding. Furthermore, bio-oil-modified mixtures exhibited optimal low-temperature cracking resistance without compromising high-temperature stability. These findings elucidate the mechanism by which bio-oil enhances the water-damage resistance of acidic aggregate systems, providing a theoretical basis for the optimized selection of sustainable asphalt modifiers. Full article

(This article belongs to the Topic Service Safety and Green Maintenance Technology for Road Infrastructure in Complex Environments)

►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-4