路面工程研究中用于检测/分类和预测目的的机器学习方法:综述

N. Karballaeezadeh, Ali Maaruof, S. DanialMohammadzadeh, Sepehr Zamani, Mohammed Mudabbiruddin
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引用次数: 0

摘要

为了维护、管理和预算道路基础设施,道路路面状况评估是必要的。测量几种路面特性以评估其状况,包括路面强度、粗糙度和表面破损。由于在该领域发表的文章迅速增长,在更深层次上对研究进行分类是很重要的。本文的目的是概述基于机器学习的路面评估研究及其对该领域的贡献。为了便于对采用类似方法的研究进行探索,研究是根据其目标进行组织的。因此,根据研究中使用的两大类目标对研究进行分类,即:1。2.以路面状况预测为目的的研究。以路面破损检测/分类为目的的研究。可以看到,第一类的研究在过去几年中发展得非常好。此外,第2类包括主要关注裂纹检测的研究,可以感觉到有必要扩大对其他类型的困扰的研究重点。
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Machine Learning Approaches for Detection/Classification and Prediction Purposes in Pavement Engineering Studies: An Overview
In order to maintain, manage, and budget for pavement infrastructure, road pavement condition assessment is necessary. Several pavement characteristics are measured to assess its condition, including pavement strength, roughness, and surface distresses. It is important to categorize studies at deeper levels due to the rapid growth of articles published in this field. The objective of this paper is to provide an overview of machine learning-based pavement evaluation studies and their contributions to the area. In order to facilitate the exploration of the studies employing similar methodologies, the studies are organized based on their goals. Therefore, studies are classified based on the two main categories of goals employed in them, namely: 1. Studies with aim of pavement condition prediction and 2. Studies with the aim of pavement distress detection/classification. It is observed that research of category 1 has grown very well during the past years. Also, category 2 includes studies that mostly focus on crack detection and it can be felt that there is a need for expanding the focus of studies on other types of distresses.
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