Automated measurement of asphalt pavement rut depth using smartphone imaging

IF 9.6 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Zhenqiang Han , Jiaqi Tang , Liqun Hu , Wei Jiang , Aimin Sha
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引用次数: 0

Abstract

Fast and accurate rutting distress detection is essential for driving safety and advancing pavement maintenance automation. However, existing Rut Depth (RD) measurement methods are often inefficient or costly due to complex pavement conditions and expensive equipment. This paper introduces a method to identify and measure RD using smartphone photography and neural networks. Accelerated Pavement Tests (APTs) were conducted to capture rutting evolution, combining measured and photographed data. Grayscale images were analyzed via Fourier transform, identifying the rut-related frequency range (0–0.02 Hz). Grayscale rut curves corresponding to actual rut cross-sections were extracted, and seven feature points were used to calculate grayscale RD. A backpropagation neural network model was trained and validated, demonstrating RD detection within 5–45 mm, with an average absolute error of 1.29 mm. This method provides an alternative for efficient RD measurement in APT and field pavement condition evaluations, offering potentials for improving pavement maintenance practices.
利用智能手机成像技术自动测量沥青路面车辙深度
快速准确的车辙损伤检测对于行车安全和推进路面养护自动化至关重要。然而,由于复杂的路面条件和昂贵的设备,现有的车辙深度(RD)测量方法往往效率低下或成本高昂。本文介绍了一种利用智能手机摄影和神经网络识别和测量RD的方法。加速路面试验(APTs)结合测量和拍摄数据,捕捉车辙演变。通过傅里叶变换对灰度图像进行分析,确定车辙相关频率范围(0-0.02 Hz)。提取实际车辙截面对应的灰度车辙曲线,利用7个特征点计算灰度RD,训练并验证了反向传播神经网络模型,实现了5-45 mm范围内的RD检测,平均绝对误差为1.29 mm。该方法为APT中有效的RD测量和现场路面状况评估提供了替代方法,为改善路面维护实践提供了潜力。
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来源期刊
Automation in Construction
Automation in Construction 工程技术-工程:土木
CiteScore
19.20
自引率
16.50%
发文量
563
审稿时长
8.5 months
期刊介绍: Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities. The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.
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