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

Understanding the Hydromechanical Effects of Extreme Events To Improve the Performance of Infrastructure Foundations

Extreme hydroclimatic events like heavy rainfall, flooding, and prolonged drought can potentially cause the failure of infrastructure foundations, leading to socio-economic losses. The objective of this dissertation is to understand the deformation and bearing capacity behavior of drilled shafts subjected to extreme hydroclimatic events, including heavy rainfall, prolonged drought, and earthquake. The Finite Element Method (FEM) results show that during rainfall, the drilled shaft settled caused by a decrease in the porewater pressure in the sand leading to a decrease in the axial bearing capacity. The axial force variation from an experimental investigation showed good agreement with the FEM. The impact of natural hazards on deep foundations can be critical and highly unpredictable when extreme hydrological and seismic events occur simultaneously or in sequence. A multi-hazard analysis was carried out to understand the structural response of deep foundations. When the drilled shaft was subjected to the dynamic load from heavy rainfall followed by dynamic load from the earthquake, the vertical settlement for the drilled shaft was significantly high compared to the case where the drilled shaft was subjected to dynamic load from the earthquake. A case study was adopted to predict the structural response of drilled shaft at the end bent of a proposed bridge subjected to liquefaction-induced lateral spreading caused by extreme earthquake events. The structural response of the bridge foundation before, during, and after liquefaction-induced lateral spreading was predicted using analytical methods and FEM. The comparison results showed that the during-liquefaction scenario was the worst-case

Finite element model updating for composite plate structures using particle swarm optimization algorithm

In the Architecture, Engineering, and Construction (AEC) industry, particularly civil engineering, the Finite Element Method (FEM) is a widely applied method for computational designs. In this regard, computational simulation has increasingly become challenging due to uncertain parameters, significantly affecting structural analysis and evaluation results, especially for composite and complex structures. Therefore, determining the exact computational parameters is crucial since the structures involve many components with different material properties, even removing some additional components affects the calculation results. This study presents a solution to increase the accuracy of the finite element (FE) model using a swarm intelligence-based approach called the particle swarm optimization (PSO) algorithm. The FE model is created based on the structure’s easily observable characteristics, in which uncertainty parameters are assumed empirically and will be updated via PSO using dynamic experimental results. The results show that the finite element model achieves high accuracy, significantly improved after updating (shown by the evaluation parameters presented in the article). In this way, a precise and reliable model can be applied to reliability analysis and structural design optimization tasks. During this research project, the FE model considering the PSO algorithm was integrated into an actual bridge’s structural health monitoring (SHM) system, which was the premise for creating the initial digital twin model for the advanced digital twinning technologyThis work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020. The authors also acknowledge ANI (“Agência Nacional de Inovação”) for the financial support given to the R&D Project “GOA Bridge Management System—Bridge Intelligence”, with reference POCI-01-0247-FEDER-069642, cofinanced by the European Regional Development Fund (FEDER) through the Operational Competitiveness and Internationalization Program (POCI).Minh Q. Tran was supported by the doctoral grant reference PRT/BD/154268/2022 financed by the Portuguese Foundation for Science and Technology (FCT), under the MIT Portugal Program (2022 MPP2030-FCT). Minh Q. Tran acknowledges Huan X. Nguyen (Faculty of Science and Technology, Middlesex University, London NW4 4BT, UK) and Thuc V. Ngo (Mien Tay Construction University, Institute of Science and International Cooperation, 85100 Vĩnh Long, Vietnam) for their support as cosupervisors as well as specific suggestions in terms of the “conceptualization” and “methodology” of this paper. Helder S. Sousa acknowledges the funding by FCT through the Scientific Employment Stimulus—4th Editio

Novel Approaches for Structural Health Monitoring

The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability and to serve their intended purpose. However, old unsolved problematics as well as new challenges exist. Furthermore, unprecedented conditions, such as stricter safety requirements and ageing civil infrastructure, pose new challenges for confrontation. Therefore, this Special Issue gathers the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring in dealing with old and new aspects of this ever-growing research field

Novel applications of pulse pre-pump Brillouin Optical Time Domain Analysis for behavior evaluation of structures under thermal and mechanical loading

This study aims to: (1) develop an analytical model for the strain transfer effect of distributed fiber optic sensors in a uniform or non-uniform stress field; (2) develop a measurement approach to monitor strains in concrete and detect damage (e.g. crack and delamination) in bonded and unbonded concrete overlays; (3) characterize the strain and temperature sensitivities of distributed fiber optic sensors at elevated temperatures; (4) develop a thermal annealing approach to enhance the thermal stability and temperature sensitivity of the distributed sensors; and (5) apply the distributed sensors to assess structural behaviors of concrete and steel structures exposed to fire. The pulse pre-pump Brillouin Optical Time Domain Analysis (PPP-BOTDA) was employed to measure strain and temperature distributions along a fused silica single-mode optical fiber. Strain distributions in concrete were measured from the distributed fiber optic sensors embedded in bonded and unbonded concrete overlays. Peaks of the strain distributions represent the effect of concrete cracks and delamination. The strain sensitivity coefficient of distributed sensors was reduced from 0.054 MHz/µε to 0.042 MHz/µε when temperature increased from 22 ⁰C to 750 ⁰C. The temperature sensitivity coefficient of distributed sensors was reduced from 1.349x10-3 GHz/⁰C to 0.419x10-3 GHz/⁰C when temperature increased from 22 ⁰C to 1000 ⁰C. The distributed sensors embedded in concrete beams measured non-uniform temperature distributions with local peaks representing a sudden increase of temperature through concrete cracks. Temperature distributions measured from the distributed sensors attached on steel beams enabled an enhanced thermo-mechanical analysis to understand the structural behaviors of steel beams subjected to fire --Abstract, page iii

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

Development of a Long-term, Multimetric Structural Health Monitoring System for a Historic Steel Truss Swing Bridge

The bridge stock across the United States is ageing, with many bridges approaching the end of their design life. The situation is so dire that the American Society of Civil Engineers gave the nation’s bridges a grade of “C+” in the 2013 edition of their Report Card on America’s Infrastructure. In fact, at the end of 2011, nearly a quarter of all bridges in the United States were classified as either structurally deficient or functionally obsolete. Thus, the nation’s bridges are in desperate need of rehabilitation and maintenance. However, limited funds are available for the repair of bridges. Management of the nation’s bridge infrastructure requires an efficient and effective use of available funds to direct the maintenance and repair efforts. Structural health monitoring has the potential to supplement the current routine of scheduled bridge inspections by providing an objective and detailed source of information about the status of the bridge. This research develops a framework for the long-term monitoring of bridges that leverages multimetric data to provide value to the bridge manager. The framework is applied to the Rock Island Arsenal Government Bridge. This bridge is a historic, steel truss, swing bridge that spans the Mississippi River between Rock Island, IL and Davenport, IA. The bridge is owned and operated by the US Army Corps of Engineers (USACE) and is a vital link for vehicular, train, and barge traffic. The USACE had a system of fiber optic strain gages installed on the bridge. As part of this research, this system was supplemented with a wireless sensor network that measured accelerations on the bridge. The multimetric data from the sensor systems was collected using a program developed in the course of this research. The data was then analyzed and metrics were developed that could be used to determine the health of the structure and the sensor networks themselves. Statistical process control methods were established to detect anomalous behavior in the short and long term time scales. Methods to locate and quantify the damage that has occurred in the structure once an anomaly has been detected were demonstrated. One of the methods developed as part of this research was a first order flexibility method. The SHM system this research develops has the desirable characteristics of being continuous temporally, multimetric, scalable, robust, autonomous, and informative. By necessity, some aspects of the developed SHM framework are unique and customized exclusively for the Rock Island Government Bridge. However, the principles developed in the framework are applicable to the development of an SHM system for any other bridge. Application of the SHM framework this research develops to other bridges has the potential to increase objectivity in the evaluation of bridges and focus maintenance efforts and funds on the bridges that are most critical to the public safety.Financial support for this research was provided in part by the Army Corps of Engineers Construction Engineering Research Laboratory (CERL) through a subcontract with Mandaree Enterprise Corporation.Ope

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

Fiber Bragg Grating Based Sensors and Systems

This book is a collection of papers that originated as a Special Issue, focused on some recent advances related to fiber Bragg grating-based sensors and systems. Conventionally, this book can be divided into three parts: intelligent systems, new types of sensors, and original interrogators. The intelligent systems presented include evaluation of strain transition properties between cast-in FBGs and cast aluminum during uniaxial straining, multi-point strain measurements on a containment vessel, damage detection methods based on long-gauge FBG for highway bridges, evaluation of a coupled sequential approach for rotorcraft landing simulation, wearable hand modules and real-time tracking algorithms for measuring finger joint angles of different hand sizes, and glaze icing detection of 110 kV composite insulators. New types of sensors are reflected in multi-addressed fiber Bragg structures for microwave–photonic sensor systems, its applications in load-sensing wheel hub bearings, and more complex influence in problems of generation of vortex optical beams based on chiral fiber-optic periodic structures. Original interrogators include research in optical designs with curved detectors for FBG interrogation monitors; demonstration of a filterless, multi-point, and temperature-independent FBG dynamical demodulator using pulse-width modulation; and dual wavelength differential detection of FBG sensors with a pulsed DFB laser