当前位置: X-MOL 学术Int. J. Pavement Eng. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Application of artificial neural network models for predicting the resilient modulus of recycled aggregates
International Journal of Pavement Engineering ( IF 3.8 ) Pub Date : 2020-07-18 , DOI: 10.1080/10298436.2020.1791863
Parisa Rahimzadeh Oskooei 1 , Alireza Mohammadinia 1 , Arul Arulrajah 1 , Suksun Horpibulsuk 2
Affiliation  

ABSTRACT

In recent years, efforts have been made to utilise construction and demolition (C&D) wastes as an alternative material to natural quarried aggregates in the structural layers of railways and roads. Resilient modulus (Mr) is one of the crucial design parameters used for the construction of roads and railways. Undertaking resilient modulus testing is expensive, time-consuming and complex. This study employs artificial neural network (ANN) as a robust method for representing two separate models for predicting the resilient modulus of bound and unbound C&D materials. A comprehensive database has been collected from past publications for developing the models. In order to consider the essential factors on determination of resilient modulus, two representative parameters are proposed. Several statistical criteria were utilised to evaluate the precision and performance of the predictive models, and the best models were transformed into practical equations. A sensitivity analysis was undertaken to determine the impact of each input parameter in the proposed models. The results indicated the applicability and efficiency of the ANN method for predicting the resilient modulus of unbound and bound C&D aggregates.



中文翻译:

人工神经网络模型在再生骨料弹性模量预测中的应用

摘要

近年来,人们努力利用建筑和拆除 (C&D) 废物作为铁路和公路结构层中天然采石骨料的替代材料。弹性模量 ( M r) 是用于公路和铁路建设的关键设计参数之一。进行弹性模量测试是昂贵、耗时且复杂的。本研究采用人工神经网络 (ANN) 作为一种稳健的方法来表示两个独立的模型,以预测结合和非结合 C&D 材料的弹性模量。从过去的出版物中收集了一个综合数据库,用于开发模型。为了考虑确定弹性模量的必要因素,提出了两个具有代表性的参数。使用几个统计标准来评估预测模型的精度和性能,并将最佳模型转化为实际方程。进行了敏感性分析以确定建议模型中每个输入参数的影响。

更新日期:2020-07-18
down
wechat
bug