{"title":"再生骨料的弹性模量和临界动应力预测:实验研究和机器学习方法","authors":"","doi":"10.1016/j.trgeo.2024.101363","DOIUrl":null,"url":null,"abstract":"<div><p>This paper aimed to investigate the feasibility of partially or completely replacing natural aggregates with recycled aggregates from construction and demolition wastes for low-carbon-emission use as coarse-grained embankment fill materials. The laboratory specimens were prepared by blending natural and recycled aggregates at varying proportions, and a series of laboratory repeated load triaxial compression tests were carried out to study the effects of material index properties and dynamic stress states on the resilient modulus and permanent strain characteristics. Based on the experimental results and by considering the main influencing parameters of the resilient modulus and permanent deformation, an artificial neural network (ANN) prediction model with optimal architecture was developed and optimized by the particle swarm optimization (PSO) algorithm, and its performance and accuracy were verified by supplementary analyses. A shakedown state classification method was proposed based on the unsupervised clustering algorithm, and a prediction model of critical dynamic stress was established based on the machine learning (ML) method and the shakedown state classification results. The research results indicate that the stress state has a greater influence on the resilient modulus and permanent deformation characteristics than other factors, and the shear stress ratio has a significant effect on the shakedown state. The resilient modulus and critical dynamic stress of such specimens vary linearly with confining pressure. The improved PSO-ANN prediction model exhibits high prediction accuracy and robustness, superior to several other commonly used ML regression prediction algorithms. The resilient modulus and critical dynamic stress prediction methods based on ML algorithms can provide technical guidance and theoretical basis for the design and in-service maintenance of similar unbound granular materials.</p></div>","PeriodicalId":56013,"journal":{"name":"Transportation Geotechnics","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of resilient modulus and critical dynamic stress of recycled aggregates: Experimental study and machine learning methods\",\"authors\":\"\",\"doi\":\"10.1016/j.trgeo.2024.101363\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper aimed to investigate the feasibility of partially or completely replacing natural aggregates with recycled aggregates from construction and demolition wastes for low-carbon-emission use as coarse-grained embankment fill materials. The laboratory specimens were prepared by blending natural and recycled aggregates at varying proportions, and a series of laboratory repeated load triaxial compression tests were carried out to study the effects of material index properties and dynamic stress states on the resilient modulus and permanent strain characteristics. Based on the experimental results and by considering the main influencing parameters of the resilient modulus and permanent deformation, an artificial neural network (ANN) prediction model with optimal architecture was developed and optimized by the particle swarm optimization (PSO) algorithm, and its performance and accuracy were verified by supplementary analyses. A shakedown state classification method was proposed based on the unsupervised clustering algorithm, and a prediction model of critical dynamic stress was established based on the machine learning (ML) method and the shakedown state classification results. The research results indicate that the stress state has a greater influence on the resilient modulus and permanent deformation characteristics than other factors, and the shear stress ratio has a significant effect on the shakedown state. The resilient modulus and critical dynamic stress of such specimens vary linearly with confining pressure. The improved PSO-ANN prediction model exhibits high prediction accuracy and robustness, superior to several other commonly used ML regression prediction algorithms. The resilient modulus and critical dynamic stress prediction methods based on ML algorithms can provide technical guidance and theoretical basis for the design and in-service maintenance of similar unbound granular materials.</p></div>\",\"PeriodicalId\":56013,\"journal\":{\"name\":\"Transportation Geotechnics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Geotechnics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214391224001843\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Geotechnics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214391224001843","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0
摘要
本文旨在研究用来自建筑和拆除废物的再生骨料部分或全部替代天然骨料作为粗粒路堤填料的低碳排放用途的可行性。通过混合不同比例的天然骨料和再生骨料制备实验室试样,并进行了一系列实验室重复加载三轴压缩试验,以研究材料指数特性和动态应力状态对弹性模量和永久应变特性的影响。根据试验结果,并考虑到弹性模量和永久变形的主要影响参数,建立了具有最优结构的人工神经网络(ANN)预测模型,并通过粒子群优化(PSO)算法对其进行了优化,通过补充分析验证了其性能和准确性。基于无监督聚类算法,提出了抖动状态分类方法,并基于机器学习(ML)方法和抖动状态分类结果,建立了临界动应力预测模型。研究结果表明,与其他因素相比,应力状态对弹性模量和永久变形特性的影响更大,剪切应力比对抖动状态有显著影响。此类试样的弹性模量和临界动应力随约束压力呈线性变化。改进后的 PSO-ANN 预测模型具有较高的预测精度和鲁棒性,优于其他几种常用的 ML 回归预测算法。基于 ML 算法的弹性模量和临界动应力预测方法可为类似非约束颗粒材料的设计和在役维护提供技术指导和理论依据。
Prediction of resilient modulus and critical dynamic stress of recycled aggregates: Experimental study and machine learning methods
This paper aimed to investigate the feasibility of partially or completely replacing natural aggregates with recycled aggregates from construction and demolition wastes for low-carbon-emission use as coarse-grained embankment fill materials. The laboratory specimens were prepared by blending natural and recycled aggregates at varying proportions, and a series of laboratory repeated load triaxial compression tests were carried out to study the effects of material index properties and dynamic stress states on the resilient modulus and permanent strain characteristics. Based on the experimental results and by considering the main influencing parameters of the resilient modulus and permanent deformation, an artificial neural network (ANN) prediction model with optimal architecture was developed and optimized by the particle swarm optimization (PSO) algorithm, and its performance and accuracy were verified by supplementary analyses. A shakedown state classification method was proposed based on the unsupervised clustering algorithm, and a prediction model of critical dynamic stress was established based on the machine learning (ML) method and the shakedown state classification results. The research results indicate that the stress state has a greater influence on the resilient modulus and permanent deformation characteristics than other factors, and the shear stress ratio has a significant effect on the shakedown state. The resilient modulus and critical dynamic stress of such specimens vary linearly with confining pressure. The improved PSO-ANN prediction model exhibits high prediction accuracy and robustness, superior to several other commonly used ML regression prediction algorithms. The resilient modulus and critical dynamic stress prediction methods based on ML algorithms can provide technical guidance and theoretical basis for the design and in-service maintenance of similar unbound granular materials.
期刊介绍:
Transportation Geotechnics is a journal dedicated to publishing high-quality, theoretical, and applied papers that cover all facets of geotechnics for transportation infrastructure such as roads, highways, railways, underground railways, airfields, and waterways. The journal places a special emphasis on case studies that present original work relevant to the sustainable construction of transportation infrastructure. The scope of topics it addresses includes the geotechnical properties of geomaterials for sustainable and rational design and construction, the behavior of compacted and stabilized geomaterials, the use of geosynthetics and reinforcement in constructed layers and interlayers, ground improvement and slope stability for transportation infrastructures, compaction technology and management, maintenance technology, the impact of climate, embankments for highways and high-speed trains, transition zones, dredging, underwater geotechnics for infrastructure purposes, and the modeling of multi-layered structures and supporting ground under dynamic and repeated loads.