Zhuoxuan Li , Jinde Cao , Hairuo Shi , Xinli Shi , Tao Ma , Wei Huang
{"title":"Roughness prediction of asphalt pavement using FGM(1,1—sin) model optimized by swarm intelligence and Markov chain","authors":"Zhuoxuan Li , Jinde Cao , Hairuo Shi , Xinli Shi , Tao Ma , Wei Huang","doi":"10.1016/j.neunet.2024.107000","DOIUrl":null,"url":null,"abstract":"<div><div>The road traffic volumes are constantly increasing worldwide, leading to significant challenges in maintaining asphalt pavements. Vehicular loads and environmental changes impact asphalt pavements, necessitating suitable predictive models. The International Roughness Index (IRI), a key indicator of road smoothness, requires IRI prediction models for performance analysis. Using the fractional accumulation operator and sine term can improve the traditional grey model’s low prediction accuracy. Then, the chaotic adaptive whale optimization algorithm and Markov chain are used to optimize the model. Based on the different asphalt pavement structures used by RIOHtrack as data for the experiments, the average RMSE, MAE, and MAPE reached 0.025, 0.020, and 1.392%, respectively. Compared with other grey models, it performs better in IRI multi-step prediction. Particularly, the proposed model can achieve compelling predictions in a small sample size only through the changes in IRI itself, which helps to evaluate road conditions and design maintenance plans.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"183 ","pages":"Article 107000"},"PeriodicalIF":6.0000,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893608024009298","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The road traffic volumes are constantly increasing worldwide, leading to significant challenges in maintaining asphalt pavements. Vehicular loads and environmental changes impact asphalt pavements, necessitating suitable predictive models. The International Roughness Index (IRI), a key indicator of road smoothness, requires IRI prediction models for performance analysis. Using the fractional accumulation operator and sine term can improve the traditional grey model’s low prediction accuracy. Then, the chaotic adaptive whale optimization algorithm and Markov chain are used to optimize the model. Based on the different asphalt pavement structures used by RIOHtrack as data for the experiments, the average RMSE, MAE, and MAPE reached 0.025, 0.020, and 1.392%, respectively. Compared with other grey models, it performs better in IRI multi-step prediction. Particularly, the proposed model can achieve compelling predictions in a small sample size only through the changes in IRI itself, which helps to evaluate road conditions and design maintenance plans.
期刊介绍:
Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.