A Synchronized Wireless Sensor Network for Experimental Modal Analysis in Structural Health Monitoring

Abstract: Structural health monitoring aims to provide an accurate diagnosis of the condition of civil infrastructures during their life span using data acquired by sensors. Wireless sensor networks represent a suitable monitoring technology to collect reliable information about the structure's condition, replacing visual inspections, and reducing installation and maintenance time and costs. This article introduces a time synchronized and configurable wireless sensor network for structural health monitoring enabling a highly accurate identification of the modal properties of the monitored structure. The wireless sensor nodes forming the network are equipped with a 3‐axis digital accelerometer and a temperature and humidity sensor. The implemented Medium Access Control layer time synchronization protocol (μ‐Sync) ensures a highly accurate synchronicity among the samples collected by the nodes, the absolute error being constantly below 10 μs, also when high sampling frequency (up to 1 kHz) and extended sampling periods (up to 10 minutes) are applied. The experimental results obtained on a wooden model bridge, compared with those derived from acceleration signals acquired by high‐quality wired sensors, show that the so synchronized wireless sensor nodes allow a precise identification of the natural frequencies of vibration of the monitored structure (1% maximum relative difference).     

Collaborative duty cycling strategies in energy harvesting sensor networks

Energy harvesting wireless sensor networks are a promising solution for low cost, long lasting civil monitoring applications. But management of energy consumption is a critical concern to ensure these systems provide maximal utility. Many common civil applications of these networks are fundamentally concerned with detecting and analyzing infrequently occurring events. To conserve energy in these situations, a subset of nodes in the network can assume active duty, listening for events of interest, while the remaining nodes enter low power sleep mode to conserve battery. However, judicious planning of the sequence of active node assignments is needed to ensure that as many nodes as possible can be reached upon the detection of an event, and that the system maintains capability in times of low energy harvesting capabilities. In this article, we propose a novel reinforcement learning (RL) agent, which acts as a centralized power manager for this system. We develop a comprehensive simulation environment to emulate the behavior of an energy harvesting sensor network, with consideration of spatially varying energy harvesting capabilities, and wireless connectivity. We then train the proposed RL agent to learn optimal node selection strategies through interaction with the simulation environment. The behavior and performance of these strategies are tested on real unseen solar energy data, to demonstrate the efficacy of the method. The deep RL agent is shown to outperform baseline approaches on both seen and unseen data.     

Hybrid wireless hull monitoring system for naval combat vessels

There is increasing interest by the naval engineering community in permanent monitoring systems that can monitor the structural behaviour of ships during their operation at sea. This study seeks to reduce the cost and installation complexity of hull monitoring systems by introducing wireless sensors into their architectural designs. Wireless sensor networks also provide other advantages over their cable-based counterparts such as adaptability, redundancy, and weight savings. While wireless sensors can enhance functionality and reduce cost, the compartmentalised layout of most ships requires some wired networking to communicate data globally throughout the ship. In this study, 20 wireless sensing nodes are connected to a ship-wide fibre-optic data network to serve as a hybrid wireless hull monitoring system on a high-speed littoral combat vessel (FSF-1 Sea Fighter). The wireless hull monitoring system is used to collect acceleration and strain data during unattended operation during a one-month period at sea. The key findings of this study include that wireless sensors can be effectively used for reliable and accurate hull monitoring. Furthermore, the fact that they are low-cost can lead to higher sensor densities in a hull monitoring system thereby allowing properties, such as hull mode shapes, to be accurately calculated.     

无线传感器网络在智能建筑中的应用

无线传感器网络系统非常适用于对布线困难、人员不能到达的区域和临时状况远程监测。介绍了无线传感器网络的概念,体系结构及节点组成,组网过程。展望了无线传感器网络在HVAC系统、照明系统、信息传输领域及环境监测等系统中的应用。最后介绍了ZigBee无线传感器网络技术和无线传感器网络应用案例。     

Scalable Structural Modal Identification Using Dynamic Sensor Network Data with STRIDEX

This article uses the formulation of the structural identification using expectation maximization (STRIDE) algorithm for compatibility with the truncated physical model (TPM) to enable scalable, output‐only modal identification using dynamic sensor network (DSN) data. The DSN data class is an adaptable and efficient technique for storing measurements from a very large number of sensing nodes, which is the case in mobile sensor networks and BIGDATA problems. In this article, the STRIDEX output‐only identification algorithm is proposed for the stochastic TPM to estimate structural modal properties (frequencies, damping ratios, and mode shapes) directly from DSN data. The spatial information produced by this novel algorithm, called STRIDEX (“X” for extended), is scalable, as demonstrated in a strategy to construct high‐resolution mode shapes from a single DSN data set using a series of independent identification runs. The ability to extract detailed structural system information from DSN data in a computationally scalable framework is a step toward mobile infrastructure informatics in a large urban setting. The performance of the STRIDEX algorithm is demonstrated, using the simulated response of a 5,000 DOF structure, and experimentally, using measurements from two mobile sensor cars, which scanned about 8,000 points on a beam specimen in the laboratory. In the experimental results, a mobile sensor is shown to provide over 120 times more mode shape points than a fixed sensor.     

Sensor Attitude Correction of Wireless Sensor Network for Acceleration‐Based Monitoring of Civil Structures

Wireless sensors are now becoming practical alternatives to traditional wired sensors in monitoring civil structures. Though many have been reported on acceleration‐based monitoring of civil structures using wireless sensor networks, sensor attitude that may be different from instrumentation plan has been a seemingly overlooked issue behind performance validation of the network. In this article, a technique to correct the sensor attitude is proposed for the wireless sensor network that measures 3D acceleration of civil structures. Six simple formulas to assess the well‐known 3D Euler angles (i.e., roll, pitch, and yaw) are derived using the gravity extracted from measured 3D acceleration and nonchanging direction of sensors on a stationary structure. The proposed technique is validated at a large‐scale wireless sensor network with 22 sensors in the respective attitudes on a truss bridge. First, attitudes assessed by the proposed method are compared with instrumentation plan. Then, mode shapes obtained before and after the correction are compared with those from finite element model. Comparison shows that quality of the mode shapes improves significantly by small amount of attitude correction less than 7°.     

Systemic reliability of bridge networks with mobile sensing-based model updating for postevent transportation decisions

This paper proposes the upscaling of conventional individual bridge health monitoring problems into urban regions and transportation networks via mobile and smart sensing techniques together with an innovative reconnaissance procedure. The paper associates structural failure probabilities with systemic features and proposes decision criteria to optimize postdisaster actions. Twenty bridges constituting transportation network infrastructure compose the testbed region and utilize smartphone accelerometers for dynamics characterization in a vibration-based framework. In this framework, reconnaissance output serves for model development, and mobile sensor data enable finite element model updating. Structural reliability analyses merged in a chain setting generate the systemic behavior of cascaded bridge performance. Combining systemic reliability with transportation and health services demand, one can optimize the response strategies of the bridge population and strategize disaster-related decisions in a postevent assessment setting. Based on a testbed region with remote access to nearby vicinities, 18 earthquake scenarios are conducted to visualize the optimal evacuation strategies on the network, taking systemic bridge performance into consideration. Cost-free mobile sensing support adds one more fundamental information source for reducing the uncertainty of the models and, therefore, improves associated mitigation actions.     

Deep learning for data anomaly detection and data compression of a long‐span suspension bridge

As intelligent sensing and sensor network systems have made progress and low‐cost online structural health monitoring has become possible and widely implemented, large quantities of highly heterogeneous data can be acquired during the monitoring. This has resulted in exceeding the capacity of traditional data analytics techniques, especially in monitoring large‐scale or critical civil structures. In particular, data storage has become a big challenge, hence, resulting in the emergence of data compression and reconstruction as a new area in structural health monitoring (SHM) of large infrastructure systems. SHM data generally include anomalies that can disturb structural analysis and assessment. The fundamental reasons for the abnormality of data are extremely complex. Therefore, reconstruction of the abnormal data is generally difficult and poses serious challenges to achieve high‐accuracy after data has been compressed. Considering these significant challenges, in this paper, a novel deep‐learning‐enabled data compression and reconstruction framework is proposed that can be divided into two phases: (a) a one‐dimensional Convolutional Neural Network (CNN) that extracts features directly from the input signals is designed to detect abnormal data with validated high accuracy; (b) a new SHM data compression and reconstruction method based on Autoencoder structure is further developed, which can recover the data with high‐accuracy under such a low compression ratio. To validate the proposed approach, acceleration data from the SHM system of a long‐span bridge in China are employed. In the abnormal data detection phase, the results show that the proposed method can detect anomaly with high accuracy. Subsequently, smaller reconstruction errors can be achieved even by using only 10% compression ratio for the normal data.     

上海“大世界”结构健康监测技术研究

文章通过使用新技术光纤光栅传感系统和无线传感系统对保护建筑大世界的结构关键部位进行健康监测,对所得数据进行分析,得到目前结构安全情况,对保护建筑结构的健康监测做了技术探索,给出保护建筑结构健康监测的示范设备。     

Wireless sensor networks as part of a web-based building environmental monitoring system

The presented research shows how advanced wireless sensor technology can be used by engineers to monitor conditions in and around buildings. The objective is split into three different tasks. First, wireless sensor hardware is programmed to process signals from sensors and transmit the data in a suitable format. This task was accomplished through an open-source operating system and a programming language designed specifically for wireless sensor hardware. The second task involved the processing of signals sent by the wireless sensor nodes. In this application, a Java program was written that deciphered messages transmitted from a wireless receiver over a computer's serial port and then placed the data in a database. The structure of that database is discussed to help identify the key pieces of information that are needed to make use of the data. The third piece of the proposed monitoring system is an interface to review the data. A Web-based system was developed that allows a user to mine the database using parameters such as the type of data, location of sensor, and the time of data acquisition. It is anticipated that this research will demonstrate the potential of using wireless sensor networks for monitoring buildings.     

Pavement performance assessment using a cost‐effective wireless accelerometer system

Pavement condition monitoring is required to identify pavements in need of maintenance or rehabilitation. Early identification of reduction in pavement's structural resistance and improving the structural resistance by minor repairs can lead to significantly lower maintenance costs for transportation agencies. In this study, a cost‐effective wireless sensor that can be embedded in the road to measure the transient vibrations due to different applied loads was tested to determine its effectiveness in terms of pavement displacement measurements. Test results show that the vibration sensor, combined with the algorithms, can be embedded in new or existing pavements and used as an accurate wireless displacement sensor. The low cost of the sensor system allows the use of these sensors at high densities for monitoring the performance of an entire road network. Outputs from the developed system can be directly used to evaluate the condition and performance of pavement structure (increasing displacement over time indicating increasing pavement damage). In addition, displacement data from the system can be used to backcalculate pavement layer stiffnesses, which can be used to predict long‐term performance of the pavement structure. Reduction in pavement layer stiffness over time can be used to determine long‐term damage accumulation.     

Simulation of Accuracy Performance for Wireless Sensor-Based Construction Asset Tracking

Abstract:   Today's mobile and remote construction applications, such as tracking of materials, equipment, and workers, demand high reliability and scalability of wireless sensor networks for a large-scale construction site. In particular, identifying the location of distributed mobile entities throughout wireless communications becomes the primary task to realize the remote tracking and monitoring of the construction assets. Even though several alternative solutions have been introduced by utilizing recent technologies, such as radio frequency identification (RFID) and the global positioning system (GPS), they could not provide a solid direction to accurate and scalable tracking frameworks in large-scale construction domains due to limited capability and inflexible networking architectures. This article introduces a new tracking architecture using wireless sensor modules and shows an accuracy performance using a numerical simulation approach based on the time-of-flight method. By combining radio frequency (RF) and ultrasound (US) signals, the simulation results showed an enhanced accuracy performance over the utilization of an RF signal only. The proposed approach can provide potential guidelines for further exploration of hardware/software design and for experimental analysis to implement the framework of tracking construction assets.     

神经网络在结构工程健康监测中的应用探讨

建立结构损伤诊断子系统是建立大型工程结构智能健康监测专家系统的核心问题。人工神经网络技术可以实现结构损伤的自动识别与定位,具有广阔的应用前景。本文介绍基于人工神经网络的两级损伤识别策略,并对采用人工神经网络进行结构损伤诊断的网络输入参数与网络结构选择等关键问题进行了探讨。     

基于物联网技术的土石坝安全监测自动化系统研究

构建了基于物联网技术的土石坝安全监测自动化系统,利用无线传感网芯片CC2530和GPRS模块完成了传感器节点、路由器节点、协调节点的硬件和软件设计,集成无线传感网和地理信息系统。实现对土石坝大坝变形、渗流、应力应变以及环境气象等各种因子进行全天候的实时监测、采集和控制,并在专家知识库辅助下,实现对土石坝安全性状的智能化、科学化管理。     

基于LoRa技术的云平台楼宇火警监测系统设计

为了解决传统楼宇火警监测系统布线复杂、传输距离短、智能化程度低等问题,结合物联网技术,设计一种基于LoRa技术的云平台楼宇火警监测系统。该系统利用LoRa技术构建无线传感网络,实现监测信息的远距离传输,包含一个汇聚节点和多个终端节点。终端节点通过多种传感器对环境信息进行监测,汇聚节点接收所有终端节点的数据,并经由EC20 4G通信模块上传至云平台。系统测试结果表明,该云平台能够实时准确地展示环境数据,异常时终端节点和云端均能及时告警,具有覆盖范围广、功耗低、成本低以及部署方便等优点。     

Real-time wireless vibration monitoring for operational modal analysis of an integral abutment highway bridge

Remote structural health monitoring systems employing a sensor-based quantitative assessment of in-service demands and structural condition are perceived as the future in long-term bridge management programs. However, the data analysis techniques and, in particular, the technology conceived years ago that are necessary for accurately and efficiently extracting condition assessment measures from highway infrastructure have just recently begun maturation. In this study, a large-scale wireless sensor network is deployed for ambient vibration testing of a single-span integral abutment bridge to derive in-service modal parameters. Dynamic behavior of the structure from ambient and traffic loads was measured with accelerometers for experimental determination of the natural frequencies, damping ratios, and mode shapes of the bridge. Real-time data collection from a 40-channel single network operating with a sampling rate of 128 Hz per sensor was achieved with essentially lossless data transmission. Successful acquisition of high-rate, lossless data on the highway bridge validates the proprietary wireless network protocol within an actual service environment. Operational modal analysis is performed to demonstrate the capabilities of the acquisition hardware with additional correlation of the derived modal parameters to a Finite Element Analysis of a model developed using as-built drawings to check plausibility of the mode shapes. Results from this testing demonstrate that wireless sensor technology has matured to the degree that modal analysis of large civil structures with a distributed network is a currently feasible and a comparable alternative to cable-based measurement approaches.     

无线传感器网络技术的研究进展及其在地铁隧道中的应用挑战

 土木工程基础设施建成后,随着使用年限增加,结构的老化问题日益凸显,结构健康监测系统被广泛采用来监测结构性能,评估结构的安全性和耐久性。城市地铁隧道结构,隐蔽于地下,受到地下水侵蚀和列车振动荷载的双重影响,健康监测工作更加重要也更加不易进行,利用无线传感器网络(WSN)进行地铁隧道结构健康监测是一个可行的解决方案。无线传感器网络与传统有线监测方式相比,在成本、尺寸、部署的灵活性、分布智能等方面都具有明显优势。同时,无线传感器网络作为一种新兴技术,在地铁隧道监测中的应用也存在一些限制,例如,隧道内的信号衰减,节点布置,时间同步,电量限制等。文中介绍无线传感器网络技术的研究进展及其在土木工程基础设施监测中的应用研究实例,分析和总结无线传感器网络在地铁隧道监测中面临的挑战,对下一步的研究方向进行展望。     

Wireless Monitoring of a Multispan Bridge Superstructure for Diagnostic Load Testing and System Identification

Abstract: This article focuses on the deployment of a wireless sensor system (WSS) developed at Clarkson University for structural monitoring purposes. The WSS is designed specifically for diagnostic bridge monitoring, providing independent conditioning for accelerometers, strain transducers, and temperature sensors in addition to high‐rate wireless data transmission and is capable of supporting large‐scale sensor arrays. A three‐span simply supported structure was subjected to diagnostic load testing as well as ambient vibration monitoring. A total of 90 wireless and several wired sensors, including accelerometers and strain transducers were used in the deployment. Strain measurements provided capacity and demand characteristics of the structure in the form of neutral axis locations, load distributions, and dynamic allowances which ultimately produced an inventory and operating load rating for the structure. Additionally, modal characteristics of the structure, including natural frequencies and mode shapes, were derived from measured accelerations and discussed briefly.     

基于贝叶斯网络的结构健康评估信息融合方法

为了充分利用大型结构健康监测系统中来自不同时间与空间的多传感器信息资源,获得被测对象的一致性决策和估计任务,进而提高确诊率,介绍了从多传感器数据融合的概念、基本原理出发,提出的一种基于贝叶斯网络数据融合技术的结构健康监测方法。重点叙述了用于结构健康检测的朴素贝叶斯网络和扩展的朴素贝叶斯网络结构构建,以及网络节点概率的确定方法,并在项目中进行了试验。基于贝叶斯网络的结构健康评估方法有效地利用了各信息源之间的互补性,提高了健康评估的准确率、可靠性和稳健性。     

基于分布式传感网络的结构模态识别方法

结构的模态参数识别是结构健康监测系统的基本任务。随着工程结构的日益大型化和复杂化,振动测试时需要布置大量的传感器。传统的集中采集和处理技术将难以胜任海量数据的处理要求,采用无线智能传感器的结构健康监测系统正是应运而生的新方向,而分布式采集和处理是其特点。在无线智能传感网络拓扑结构中采用分布式算法求解结构整体振型,利用随机子空间法识别各子结构模态,结合粒子群优化算法调整子振型获取结构整体振型。通过混凝土钢管拱桥模型试验验证了分布式算法的可行性,并利用模态置信度(MAC)对比分析了由分布式模态识别方法和集中式模态识别方法得到的结果,结果表明两种算法吻合较好。