{"title":"Evolution of prediction models for road surface irregularity: Trends, methods and future","authors":"Yanan Wu, Yafeng Pang, Xingyi Zhu","doi":"10.1016/j.conbuildmat.2024.138316","DOIUrl":null,"url":null,"abstract":"<div><p>In the modern era, the importance of prioritizing traffic safety has become increasingly evident, requiring dedicated focus. An effective strategy for improving traffic safety involves optimizing road roughness to minimize road bumps and mitigate the risk of accidents. Currently, artificial intelligence algorithms are widely recognized for their capacity to accurately forecast pavement roughness in intricate environments. The current state of research on road roughness prediction using artificial intelligence approaches is found to be deficient in providing a comprehensive review. This paper aims to provide a comprehensive analysis of the patterns in predicting pavement roughness using artificial intelligence algorithms through a systematic review. This article provides an overview of the development process of IRI prediction and introduces commonly used artificial intelligence methods in the road field. These methods are primarily categorized into machine learning and deep learning. The article also presents a comprehensive overview of the similarities and differences among various works in this domain. Regarding the issue of data sources, it is divided into LTPP database and other databases, summarizing the data sources and volume used in the literature, as well as independent variables including road age, material property, road performance, climate parameters, etc. The challenges and future perspective in predicting road International Roughness Index (IRI) for the future are proposed, taking into consideration the complexity of data collection and limitations on the development of artificial intelligence networks.</p></div>","PeriodicalId":288,"journal":{"name":"Construction and Building Materials","volume":"449 ","pages":"Article 138316"},"PeriodicalIF":7.4000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Construction and Building Materials","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950061824034585","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
In the modern era, the importance of prioritizing traffic safety has become increasingly evident, requiring dedicated focus. An effective strategy for improving traffic safety involves optimizing road roughness to minimize road bumps and mitigate the risk of accidents. Currently, artificial intelligence algorithms are widely recognized for their capacity to accurately forecast pavement roughness in intricate environments. The current state of research on road roughness prediction using artificial intelligence approaches is found to be deficient in providing a comprehensive review. This paper aims to provide a comprehensive analysis of the patterns in predicting pavement roughness using artificial intelligence algorithms through a systematic review. This article provides an overview of the development process of IRI prediction and introduces commonly used artificial intelligence methods in the road field. These methods are primarily categorized into machine learning and deep learning. The article also presents a comprehensive overview of the similarities and differences among various works in this domain. Regarding the issue of data sources, it is divided into LTPP database and other databases, summarizing the data sources and volume used in the literature, as well as independent variables including road age, material property, road performance, climate parameters, etc. The challenges and future perspective in predicting road International Roughness Index (IRI) for the future are proposed, taking into consideration the complexity of data collection and limitations on the development of artificial intelligence networks.
在现代社会,交通安全优先的重要性日益明显,需要我们全神贯注。改善交通安全的有效策略包括优化路面粗糙度,以尽量减少路面颠簸,降低事故风险。目前,人工智能算法因其能够准确预测复杂环境中的路面粗糙度而得到广泛认可。目前,利用人工智能方法预测路面粗糙度的研究还不够全面。本文旨在通过系统综述,全面分析利用人工智能算法预测路面粗糙度的规律。本文概述了 IRI 预测的发展过程,并介绍了道路领域常用的人工智能方法。这些方法主要分为机器学习和深度学习。文章还全面概述了该领域各种工作的异同。关于数据来源问题,文章分为 LTPP 数据库和其他数据库,总结了文献中使用的数据来源和数量,以及自变量,包括道路年龄、材料属性、道路性能、气候参数等。考虑到数据收集的复杂性和人工智能网络发展的局限性,提出了未来预测道路国际粗糙度指数(IRI)的挑战和未来展望。
期刊介绍:
Construction and Building Materials offers an international platform for sharing innovative and original research and development in the realm of construction and building materials, along with their practical applications in new projects and repair practices. The journal publishes a diverse array of pioneering research and application papers, detailing laboratory investigations and, to a limited extent, numerical analyses or reports on full-scale projects. Multi-part papers are discouraged.
Additionally, Construction and Building Materials features comprehensive case studies and insightful review articles that contribute to new insights in the field. Our focus is on papers related to construction materials, excluding those on structural engineering, geotechnics, and unbound highway layers. Covered materials and technologies encompass cement, concrete reinforcement, bricks and mortars, additives, corrosion technology, ceramics, timber, steel, polymers, glass fibers, recycled materials, bamboo, rammed earth, non-conventional building materials, bituminous materials, and applications in railway materials.