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用于在行驶通过条件下评估桥梁挠度的人工智能增强物联网系统。

AI-Enhanced IoT System for Assessing Bridge Deflection in Drive-By Conditions.

作者信息

Iacussi Leonardo, Chiariotti Paolo, Cigada Alfredo

机构信息

Department of Mechanical Engineering, Politecnico di Milano, Via Privata Giuseppe la Masa 1, 20156 Milano, Italy.

出版信息

Sensors (Basel). 2024 Dec 30;25(1):158. doi: 10.3390/s25010158.

DOI:10.3390/s25010158
PMID:39796949
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11722870/
Abstract

The increasing traffic on roads poses a significant challenge to the structural integrity of bridges and viaducts. Indirect structural monitoring offers a cost-effective and efficient solution for monitoring multiple infrastructures. The presented work aims to explore new sensing strategies based on digital MEMS sensors integrated into an intelligent IoT infrastructure to predict the bridge deflection behaviour for indirect Bridge Structural Health Monitoring purposes. An experimental setup comprising a bridge model and vehicle equipped with a smart sensing node has been used to generate the dataset. Various models for bridge deflection estimation are deployed on the sensorized vehicle, exploiting edge AI capabilities of smart sensors. This study shows the potential of leveraging data-driven technologies to enhance the performance of low-cost sensors. Additionally, it demonstrates the viability of assessing static deflection shapes of bridges through indirect measurements on board vehicles, underlining the potential of this approach to make SHM more cost-effective and scalable.

摘要

道路上日益增加的交通流量对桥梁和高架桥的结构完整性构成了重大挑战。间接结构监测为监测多个基础设施提供了一种经济高效的解决方案。本文提出的工作旨在探索基于集成到智能物联网基础设施中的数字MEMS传感器的新传感策略，以预测桥梁挠度行为，用于间接桥梁结构健康监测。一个由桥梁模型和配备智能传感节点的车辆组成的实验装置被用来生成数据集。用于桥梁挠度估计的各种模型被部署在装有传感器的车辆上，利用智能传感器的边缘人工智能能力。这项研究展示了利用数据驱动技术提高低成本传感器性能的潜力。此外，它还证明了通过在车辆上进行间接测量来评估桥梁静态挠度形状的可行性，强调了这种方法使结构健康监测更具成本效益和可扩展性的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/be2a51e09825/sensors-25-00158-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/bf8b16a46bb0/sensors-25-00158-g007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/bf8d740e75da/sensors-25-00158-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/be2a51e09825/sensors-25-00158-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/236a2206e02e/sensors-25-00158-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/2dd859851db5/sensors-25-00158-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/9417c624e410/sensors-25-00158-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/dd8fc5adc84f/sensors-25-00158-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/c856b5156f36/sensors-25-00158-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/21e395491c62/sensors-25-00158-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/bf8b16a46bb0/sensors-25-00158-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/1befe9427db2/sensors-25-00158-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/e86162f67fda/sensors-25-00158-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/843f68523d30/sensors-25-00158-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/bf8d740e75da/sensors-25-00158-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4514/11722870/be2a51e09825/sensors-25-00158-g012.jpg

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IMU/UWB Fusion Method Using a Complementary Filter and a Kalman Filter for Hybrid Upper Limb Motion Estimation.一种使用互补滤波器和卡尔曼滤波器的IMU/UWB融合方法用于混合上肢运动估计
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