VibWall:智能手机对墙体裂缝检测的振动挑战响应

Wei Sun
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引用次数: 0

摘要

随着建筑物的老化,由于环境(如温度和湿度)的变化,墙体结构可能会恶化(如墙体裂缝、不连续和腐蚀)。此外,这些墙面裂缝、不连续和腐蚀会影响居住的舒适性和舒适感。因此,墙体健康诊断对现代建筑的安全性和舒适性至关重要。然而,现有的墙体健康检测技术(如超宽带雷达、声传感和传感器嵌入技术)成本高,不普遍,对环境变化的鲁棒性差。在本文中,我们提出了VibWall,一个可以使用智能手机的传感器(即加速度计,陀螺仪和振动器)来检测墙壁结构健康的系统。具体来说,可以检测墙体裂缝,以确保居住安全、舒适和舒适。我们的主要想法是,智能手机的振动会根据墙壁的物理结构被不同程度地吸收、反射和传播。具体来说,我们采用了一种新的挑战-响应方案,其中挑战是来自智能手机振动器的一系列异质振动模式,智能手机的陀螺仪和加速度计传感器可以感知这些振动的响应。然后,基于机器学习的分类器(例如,随机森林分类器)将用于根据这些响应区分健康墙壁和具有裂缝,不连续或腐蚀的墙壁。实验结果表明,用墙体样品和真实墙体进行墙体结构健康检测具有较好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VibWall: Smartphone’s Vibration Challenge-response for Wall Crack Detection
As the building ages, the wall structure may become deteriorated (e.g., wall cracks, discontinuities, and corrosion) due to the variation of the environment (i.e., temperature and humidity). Moreover, these wall cracks, discontinuities, and corrosion will affect the living comfort and coziness. As such, the wall health diagnostic becomes crucial for the safety and comfort of modern buildings. However, the existing wall health detection techniques (e.g., UWB radars, acoustic sensing, and sensor embedding techniques) are high-cost, not ubiquitous, and not robust to the variation of the environment. In this article, we propose VibWall, a system that can use the smartphone’s sensors (i.e., accelerometer, gyroscope, and vibrator) to detect the wall’s structural health. Specifically, the wall cracks can be detected for living safety, comfort, and coziness. Our key idea is that the smartphone’s vibration is absorbed, reflected, and propagated disparately based on the physical structure of the wall. To be specific, we employ a novel challenge-response scheme, where the challenge is a sequence of heterogeneous vibration patterns from the smartphone’s vibrator, and the responses to these vibrations are sensed by the smartphone’s gyroscope and accelerometer sensors. Then, the machine learning-based classifier (e.g., random forest classifier) will be used to discriminate between the healthy wall and the wall with cracks, discontinuities, or corrosion based on these responses. Our experimental results show good performance on the wall’s structural health detection with the wall specimen and real-world walls.
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