Symmetry in Structural Health Monitoring

In this Special Issue on symmetry, we mainly discuss the application of symmetry in various structural health monitoring. For example, considering the health monitoring of a known structure, by obtaining the static or dynamic response of the structure, using different signal processing methods, including some advanced filtering methods, to remove the influence of environmental noise, and extract structural feature parameters to determine the safety of the structure. These damage diagnosis methods can also be effectively applied to various types of infrastructure and mechanical equipment. For this reason, the vibration control of various structures and the knowledge of random structure dynamics should be considered, which will promote the rapid development of the structural health monitoring. Among them, signal extraction and evaluation methods are also worthy of study. The improvement of signal acquisition instruments and acquisition methods improves the accuracy of data. A good evaluation method will help to correctly understand the performance with different types of infrastructure and mechanical equipment

Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures

In the past, when elements in sructures were composed of perishable materials, such as wood, the maintenance of houses, bridges, etc., was considered of vital importance for their safe use and to preserve their efficiency. With the advent of materials such as reinforced concrete and steel, given their relatively long useful life, periodic and constant maintenance has often been considered a secondary concern. When it was realized that even for structures fabricated with these materials that the useful life has an end and that it was being approached, planning maintenance became an important and non-negligible aspect. Thus, the concept of structural health monitoring (SHM) was introduced, designed, and implemented as a multidisciplinary method. Computational mechanics, static and dynamic analysis of structures, electronics, sensors, and, recently, the Internet of Things (IoT) and artificial intelligence (AI) are required, but it is also important to consider new materials, especially those with intrinsic self-diagnosis characteristics, and to use measurement and survey methods typical of modern geomatics, such as satellite surveys and highly sophisticated laser tools

Bridge Structrural Health Monitoring Using a Cyber-Physical System Framework

Highway bridges are critical infrastructure elements supporting commercial and personal traffic. However, bridge deterioration coupled with insufficient funding for bridge maintenance remain a chronic problem faced by the United States. With the emergence of wireless sensor networks (WSN), structural health monitoring (SHM) has gained increasing attention over the last decade as a viable means of assessing bridge structural conditions. While intensive research has been conducted on bridge SHM, few studies have clearly demonstrated the value of SHM to bridge owners, especially using real-world implementation in operational bridges. This thesis first aims to enhance existing bridge SHM implementations by developing a cyber-physical system (CPS) framework that integrates multiple SHM systems with traffic cameras and weigh-in-motion (WIM) stations located along the same corridor. To demonstrate the efficacy of the proposed CPS, a 20-mile segment of the northbound I-275 highway in Michigan is instrumented with four traffic cameras, two bridge SHM systems and a WIM station. Real-time truck detection algorithms are deployed to intelligently trigger the SHM systems for data collection during large truck events. Such a triggering approach can improve data acquisition efficiency by up to 70% (as compared to schedule-based data collection). Leveraging computer vision-based truck re-identification techniques applied to videos from the traffic cameras along the corridor, a two-stage pipeline is proposed to fuse bridge input data (i.e. truck loads as measured by the WIM station) and output data (i.e. bridge responses to a given truck load). From August 2017 to April 2019, over 20,000 truck events have been captured by the CPS. To the author’s best knowledge, the CPS implementation is the first of its kind in the nation and offers large volume of heterogeneous input-output data thereby opening new opportunities for novel data-driven bridge condition assessment methods. Built upon the developed CPS framework, the second half of the thesis focuses on use of the data in real-world bridge asset management applications. Long-term bridge strain response data is used to investigate and model composite action behavior exhibited in slab-on-girder highway bridges. Partial composite action is observed and quantified over negative bending regions of the bridge through the monitoring of slip strain at the girder-deck interface. It is revealed that undesired composite action over negative bending regions might be a cause of deck deterioration. The analysis performed on modeling composite action is a first in studying composite behavior in operational bridges with in-situ SHM measurements. Second, a data-driven analytical method is proposed to derive site-specific parameters such as dynamic load allowance and unit influence lines for bridge load rating using the input-output data. The resulting rating factors more rationally account for the bridge's systematic behavior leading to more accurate rating of a bridge's load-carrying capacity. Third, the proposed CPS framework is shown capable of measuring highway traffic loads. The paired WIM and bridge response data is used for training a learning-based bridge WIM system where truck weight characteristics such as axle weights are derived directly using corresponding bridge response measurements. Such an approach is successfully utilized to extend the functionality of an existing bridge SHM system for truck weighing purposes achieving precision requirements of a Type-II WIM station (e.g. vehicle gross weight error of less than 15%).PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163210/1/rayhou_1.pd

Cloud-based digital twinning for structural health monitoring using deep learning

Digital Twin technology has recently gathered pace in the engineering communities as it allows for the convergence of the real structure and its digital counterpart throughout their entire life-cycle. With the rapid development of supporting technologies, including machine learning, 5G/6G, cloud computing, and Internet of Things, Digital Twin has been moving progressively from concept to practice. In this paper, a Digital Twin framework based on cloud computing and deep learning for structural health monitoring is proposed to efficiently perform real-time monitoring and proactive maintenance. The framework consists of structural components, device measurements, and digital models formed by combining different sub-models including mathematical, finite element, and machine learning ones. The data interaction among physical structure, digital model, and human interventions are enhanced by using cloud computing infrastructure and a user-friendly web application. The feasibility of the proposed framework is demonstrated via case studies of damage detection of model bridge and real bridge structures using deep learning algorithms, with high accuracy of 92%

Investigation of Computer Vision Concepts and Methods for Structural Health Monitoring and Identification Applications

This study presents a comprehensive investigation of methods and technologies for developing a computer vision-based framework for Structural Health Monitoring (SHM) and Structural Identification (St-Id) for civil infrastructure systems, with particular emphasis on various types of bridges. SHM is implemented on various structures over the last two decades, yet, there are some issues such as considerable cost, field implementation time and excessive labor needs for the instrumentation of sensors, cable wiring work and possible interruptions during implementation. These issues make it only viable when major investments for SHM are warranted for decision making. For other cases, there needs to be a practical and effective solution, which computer-vision based framework can be a viable alternative. Computer vision based SHM has been explored over the last decade. Unlike most of the vision-based structural identification studies and practices, which focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation, the proposed framework combines the vision-based structural input and the structural output from non-contact sensors to overcome the limitations given above. First, this study develops a series of computer vision-based displacement measurement methods for structural response (structural output) monitoring which can be applied to different infrastructures such as grandstands, stadiums, towers, footbridges, small/medium span concrete bridges, railway bridges, and long span bridges, and under different loading cases such as human crowd, pedestrians, wind, vehicle, etc. Structural behavior, modal properties, load carrying capacities, structural serviceability and performance are investigated using vision-based methods and validated by comparing with conventional SHM approaches. In this study, some of the most famous landmark structures such as long span bridges are utilized as case studies. This study also investigated the serviceability status of structures by using computer vision-based methods. Subsequently, issues and considerations for computer vision-based measurement in field application are discussed and recommendations are provided for better results. This study also proposes a robust vision-based method for displacement measurement using spatio-temporal context learning and Taylor approximation to overcome the difficulties of vision-based monitoring under adverse environmental factors such as fog and illumination change. In addition, it is shown that the external load distribution on structures (structural input) can be estimated by using visual tracking, and afterward load rating of a bridge can be determined by using the load distribution factors extracted from computer vision-based methods. By combining the structural input and output results, the unit influence line (UIL) of structures are extracted during daily traffic just using cameras from which the external loads can be estimated by using just cameras and extracted UIL. Finally, the condition assessment at global structural level can be achieved using the structural input and output, both obtained from computer vision approaches, would give a normalized response irrespective of the type and/or load configurations of the vehicles or human loads

D5.1 SHM digital twin requirements for residential, industrial buildings and bridges

This deliverable presents a report of the needs for structural control on buildings (initial imperfections, deflections at service, stability, rheology) and on bridges (vibrations, modal shapes, deflections, stresses) based on state-of-the-art image-based and sensor-based techniques. To this end, the deliverable identifies and describes strategies that encompass state-of-the-art instrumentation and control for infrastructures (SHM technologies).Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin

Framework of damage detection in vehicle-bridge coupled system and application to bridge scour monitoring

Most vibration-based damage identification methods make use of measurements directly from bridge structures with attached sensors. However, the vehicle moving on the bridge can serve as both an active actuator and a response receiver. This dissertation aimed to develop new methodologies to eventually detect bridge damages such as scour using the dynamic response of the vehicle. To reach the final objective, a framework of damage identification was developed first, which gave a guideline on the three crucial steps for damage detection. An optimization method was proposed that combines the Genetic Algorithm (GA) and the First Order (FO) method. It has the advantages of the global and local algorithms and converges faster than the traditional method using any initial values. Secondly, a new methodology using the transmissibility of vehicle and bridge responses was developed to detect bridge damages. The transmissibility of a simplified vehicle-bridge coupled (VBC) system was analyzed theoretically and numerically to study the feasibility of this method. To obtain the transmissibility, two methods were proposed using two “static” vehicles on the bridge. Then, a tractor-trailer test system was designed to obtain reliable responses and extract bridge modal properties from the dynamic response of moving vehicles. The test vehicle consists of a tractor and two following trailers. The residual responses of the two trailers were used, which successfully eliminated the roughness and vehicle driving effect and extracted the bridge modal properties. This methodology was applied on a field bridge and revealed a good performance. Most previous studies of bridge scour focus on the scour causes instead of its consequences. Finally, in this dissertation the developed methodologies were applied to detect scour damage from the response of bridge and/or vehicles. The scour effect on a single pile was studied and methods of scour damage detections were proposed. A monitoring system using fiber optic sensors was designed and tested in the laboratory and is being applied to a field bridge. Finally, the scour effect on the response of the entire bridge and the traveling vehicle was also investigated under the bridge-vehicle-wave interaction, which in turn was used to detect the bridge scour

Active thermography for the investigation of corrosion in steel surfaces

The present work aims at developing an experimental methodology for the analysis of corrosion phenomena of steel surfaces by means of Active Thermography (AT), in reflexion configuration (RC). The peculiarity of this AT approach consists in exciting by means of a laser source the sound surface of the specimens and acquiring the thermal signal on the same surface, instead of the corroded one: the thermal signal is then composed by the reflection of the thermal wave reflected by the corroded surface. This procedure aims at investigating internal corroded surfaces like in vessels, piping, carters etc. Thermal tests were performed in Step Heating and Lock-In conditions, by varying excitation parameters (power, time, number of pulse, ….) to improve the experimental set up. Surface thermal profiles were acquired by an IR thermocamera and means of salt spray testing; at set time intervals the specimens were investigated by means of AT. Each duration corresponded to a surface damage entity and to a variation in the thermal response. Thermal responses of corroded specimens were related to the corresponding corrosion level, referring to a reference specimen without corrosion. The entity of corrosion was also verified by a metallographic optical microscope to measure the thickness variation of the specimens

Modeling and Monitoring of the Dynamic Response of Railroad Bridges using Wireless Smart Sensors

Railroad bridges form an integral part of railway infrastructure in the USA, carrying approximately 40 % of the ton-miles of freight. The US Department of Transportation (DOT) forecasts current rail tonnage to increase up to 88 % by 2035. Within the railway network, a bridge occurs every 1.4 miles of track, on average, making them critical elements. In an effort to accommodate safely the need for increased load carrying capacity, the Federal Railroad Association (FRA) announced a regulation in 2010 that the bridge owners must conduct and report annual inspection of all the bridges. The objective of this research is to develop appropriate modeling and monitoring techniques for railroad bridges toward understanding the dynamic responses under a moving train. To achieve the research objective, the following issues are considered specifically. For modeling, a simple, yet effective, model is developed to capture salient features of the bridge responses under a moving train. A new hybrid model is then proposed, which is a flexible and efficient tool for estimating bridge responses for arbitrary train configurations and speeds. For monitoring, measured field data is used to validate the performance of the numerical model. Further, interpretation of the proposed models showed that those models are efficient tools for predicting response of the bridge, such as fatigue and resonance. Finally, fundamental software, hardware, and algorithm components are developed for providing synchronized sensing for geographically distributed networks, as can be found in railroad bridges. The results of this research successfully demonstrate the potentials of using wirelessly measured data to perform model development and calibration that will lead to better understanding the dynamic responses of railroad bridges and to provide an effective tool for prediction of bridge response for arbitrary train configurations and speeds.National Science Foundation Grant No. CMS-0600433National Science Foundation Grant No. CMMI-0928886National Science Foundation Grant No. OISE-1107526National Science Foundation Grant No. CMMI- 0724172 (NEESR-SD)Federal Railroad Administration BAA 2010-1 projectOpe