Structural Health Monitoring Program Research Articles

Elimination of Thermal Effects from Limited Structural Displacements Based on Remote Sensing by Machine Learning Techniques

Confounding variability caused by environmental and/or operational conditions is a big challenge in the structural health monitoring (SHM) of large-scale civil structures. The elimination of such variability is of paramount importance in avoiding economic and human losses. Machine learning-aided data normalization provides a good solution to this challenge. Despite proper studies on data normalization using structural responses/features acquired from contact-based sensors, this issue has not been explored properly via new features, such as displacement responses from remote sensing products, including synthetic aperture radar (SAR) images. Hence, the main aim of this work was to eliminate environmental variability, particularly thermal effects, from different and limited structural displacements retrieved from a few SAR images related to long-term health monitoring programs of long-span bridges. For this purpose, we conducted a comprehensive comparative study to investigate two supervised and two unsupervised data normalization algorithms. The supervised algorithms were based on Gaussian process regression (GPR) and support vector regression (SVR), for which temperature records acquired from contact temperature sensors and structural displacements retrieved from spaceborne remote sensors produce univariate predictor (input) and response (output) data for the regression problem. For the unsupervised algorithms, this paper employed principal component analysis (PCA) and proposed a deep autoencoder (DAE), both of which conform with unsupervised reconstruction-based data normalization. In contrast to the GPR- and SVR-based data normalization algorithms, both the PCA and DAE methods only consider the SAR-based displacement (output) data without any requirement of the environmental and/or operational (input) data. Limited displacement sets of long-span bridges from a few SAR images of Sentinel-1A, related to long-term SHM programs, were considered to assess the aforementioned techniques. Results demonstrate that the proposed DAE-aided data normalization is the best approach to remove thermal effects and other unmeasured environmental and/or operational variability.

Mexico City during and after the September 19, 2017 earthquake: Assessment of seismic resilience and ongoing recovery process.

Mexico City was severely affected by the September 19, 2017 Puebla-Morelos earthquake (Mw = 7.1). City authorities confirmed that more than 12,000 structures for all uses were damaged as a consequence of this earthquake. In this paper, the focus of attention is devoted to trying to quantify in a simple manner how resilient the built environment in Mexico City was during the September 19, 2017 earthquake. Global statistics compiled for the severity of damage observed during this seismic event from detailed information gathered from well-documented and detailed damage surveys were used for this purpose. Also, an update is provided on how the seismic reconstruction and recovery processes of the built environment in Mexico City have been after this earthquake. This adaptive resilience has been assessed from reliable information and statistics of the ongoing reconstruction process of the affected built environment in Mexico City. The implementation of structural health monitoring programs for typical, representative buildings within the city would be germane to detect and correct potential structural deficiencies on time before the next strong earthquake may strike, then helping to improve the seismic resilience of the built environment.

In-situ load testing of a WWII era timber Warren truss in the development of a structural health monitoring program

This research paper presents the experimental results of an in-situ load-testing program within a WWII era, timber Warren truss structure located at Canadian Forces Base St-Jean, Quebec. Four point load tests were carried out on a 68 m long, Douglas fir timber truss instrumented with conventional electric strain gauges, distributed fibre optic sensors, and string potentiometers, among other instruments. These instruments makeup a larger, long-term structural health monitoring program implemented within the truss structure. The objectives of the local point load tests were to (1) confirm the in-situ monitoring instruments are in working order, (2) compare calculated mechanical properties with assumed properties, and (3) calibrate a numerical model capable of replicating truss response and compare to current modeling standards used within the Department of National Defence. Non-destructive testing, including moisture content and cable tension tests, was also conducted to determine existing material properties. Load testing and non-destructive testing results identified variations between the existing properties of the truss and the assumed properties of certain structural members, as well as identifying possible lower strength or stiffness values of stress-reversed members. Results also indicated the importance of analyzing timber as an orthotropic, bimodular material for reliability and conservatism, particularly in numerical modeling. Lastly, the results presented in this paper outline the importance of in-situ load testing with an aim to determine existing material properties for monitoring projects. Moreover, the identification of the existing properties and behaviour of the structure is fundamental in the development of a structural health monitoring program for emergency purposes.

Assessment of low-cost wireless sensors for structural health monitoring applications

Abstract Structural Health Monitoring (SHM) programs play an essential task in the field of civil engineering, especially for assessing safety conditions involving large structures such as viaducts, bridges, stadiums, and tall buildings. In fact, some of these structures are monitored 24 hours a day, 7 days a week, to supply dynamic measurements that can be used for the identification of structural problems, e.g., presence of cracks, excessive vibration, damage, among others. SHM programs may provide automated assessment of structural health by processing vibration data obtained from sensors attached to the structure. Frequently, SHM uses wired systems, which are usually expensive due to the necessity of continuous maintenance and are not always suitable for sensing remote structures. Conversely, commercial wireless systems often demand high implementation costs. Hence, this paper proposes the use of a low-cost wireless sensing system based on the single board computer Raspberry Pi, which significantly reduces implementation expenses while keeping data’s integrity. The wireless communication is performed in real-time through a local wireless network, responsible for sending and receiving vibration data. The proposed system is validated by comparing its results with a commercial wired system through a series of controlled experimental applications. The results suggest that the proposed system is suitable for civil SHM applications.

A methodological approach for structural health monitoring of mass-timber buildings under construction

Structural health monitoring (SHM) is a method used to evaluate the performance of new structural systems and critical infrastructure. With mass-timber building construction on the rise, SHM programs have emerged to document hygrothermal, static, and dynamic behavior of these structures. To most efficiently document behavior and provide recommendations to industry, it is key that the research community work collaboratively to create consistent data by using standardized approaches. This paper presents a methodological approach for monitoring mass-timber buildings during construction to address this need. The approach was validated over ten months with a mass-timber building under construction at Oregon State University.

Early damage assessment in large-scale structures by innovative statistical pattern recognition methods based on time series modeling and novelty detection

Time series analysis and novelty detection are effective and promising methods for data-driven structural health monitoring (SHM) based on the statistical pattern recognition paradigm. However, processing substantially large volumes of vibration measurements may represent a serious limitation, especially for long-term SHM programs of large-scale civil structures. Moreover, shortcomings like the choice of an appropriate time series model in an automatic manner, the determination of optimal orders of the identified model and the classification of random high-dimensional features for damage detection, can strongly affect the performance of these approaches. This study is intended to propose statistical pattern recognition methods regarding time series modeling for feature extraction and novelty detection in feature classification in the presence of big data. These methods include an automatic model identification algorithm, an improved order determination approach and a hybrid distance-based novelty detection through a combination of Partition-based Kullback-Leibler divergence and Mahalanobis-squared distance. Experimental datasets relevant to a cable-stayed bridge are considered to validate the effectiveness of the proposed methods. Results demonstrate that: the AutoRegressive-AutoRegressive with eXogenous input (AR-ARX) model turns out to be the most suitable representation for feature extraction; the orders of this model are efficiently and automatically determined; the proposed novelty detection approach is highly successful in detecting damage, even in case of large volumes of random high-dimensional features.

Identification of Vortex-Induced Vibration of Tall Building Pinnacle Using Cluster Analysis for Fatigue Evaluation: Application to Burj Khalifa

Pinnacles on top of tall buildings are vulnerable to vortex-induced vibrations (VIVs). These structures may undergo large-amplitude vibrations that can lead to fatigue damage accumulation. To assess the performance of buildings and its appendages, numerous structural health monitoring (SHM) programs have been installed on tall buildings. This continuous monitoring generates more than 1 trillion data points per year per building. Also, on many occasions, the data generated by SHM programs contain missing observations. The evaluation of fatigue life using conventional methods becomes an impossible task in this case. This paper introduces the use of machine-learning techniques as a potential solution to deal with the burgeoning data generated by tall building monitoring systems. In particular, the present study involves the evaluation of the crosswind fatigue life of the pinnacle of Burj Khalifa subject to VIVs using cluster analysis. This unsupervised machine-learning technique is used to develop a generalized framework robust to missing data to effectively identify and extract VIVs from a large pool of other responses recorded by the monitoring system. The data generated from 2010 to 2014 by the SmartSync monitoring system installed on Burj Khalifa are utilized for this study. The proposed framework is validated using a wind tunnel dataset of a bridge sectional model undergoing VIVs. The VIVs extracted from the SmartSync system through cluster analysis are used to evaluate the crosswind fatigue damage of the pinnacle of Burj Khalifa using conventional closed-form approximations. The proposed cluster analysis framework uses a step-by-step data-driven decision-making approach, thus widening the applicability of the method to other SHM programs.

Diagnostic Testing of a Vertical Lift Truss Bridge for Model Verification and Decision-Making Support

A practical long term structural health monitoring program must be based on reasonable field capabilities, the needs of the bridge owner, and the anticipated structural behavior unique to the structure. Each sensor installed as part of the monitoring program should provide information directly in response to an operational needs related to the structure’s short term and long term performance. A thoughtfully considered instrumentation plan developed in cooperation with the bridge designer, bridge manager, bridge maintenance operator and academic researchers will provide data to enhance both the state of the practice and state of the art for the bridge structural design, management and maintenance. Monitoring a bridge’s structural response has the potential to a) detect the presence of structural changes for condition assessment, b) inform the bridge manger to assist in daily operational decision-making and c) validate the structural design assumptions and (d) refine a structural model of the bridge to be used for performance prediction. The excitation for these responses typically comes from traffic or environmental demands. Vertical lift bridges provide a unique opportunity for structural health monitoring based on the dynamic response due to the frequent and repeated impact imparted on the structure each time the lift span open and closes. In this paper, a structural health monitoring system designed to provide valuable information for design verification, structural model calibration, fatigue monitoring, and operational decision-making support for the reconstructed Memorial Bridge carrying US Route 1 between Portsmouth, New Hampshire and Kittery, Maine. This paper will detail the development of the sensor layout including input from stakeholders, accessibility issues and complementary and contradicting objectives. A set of structural models with varying degrees on complexity were created based on the structural performance objectives. The data collected during a pseudo-static truck load test was used to calibrate the structural models of the bridge and to select the appropriate model for each post-processing and decision-making tools related to structural performance.

Unsupervised real‐time SHM technique based on novelty indexes

Structural health monitoring programs play an essential role in the field of civil engineering, especially for assessing safety conditions involving large structures such as viaducts, bridges, tall buildings, towers, and old historical buildings. Mostly, an SHM process needs to be based on a trustful strategy for detecting structural novelties or abnormal behaviors. Usually, such an approach is complemented with human inspection and structural instrumentation routines, where the latter requires proper hardware equipment and software tools. Recently, many advances were achieved regarding the hardware resources, such as wireless communication, remotely configurable sensors, and other data management devices. On the other hand, the software counterpart still is in its early developments. Several researches are in progress to fill this gap. In this context, this paper presents a novel online SHM identification method suitable to unsupervised real-time detection of abnormal structural behaviors. The proposed methodology includes the use of an original representation of raw dynamic signals, that is, in situ measured accelerations. To assess the proposed approach, numerical simulations and two experimental applications are studied: a railway viaduct, PK 075+317 in France and an old masonry tower in Italy. The results suggest that the proposed detection indexes are suitable for a wide range of SHM applications.

Structural monitoring and identification of civil infrastructure in the United States

Monitoring the performance and estimating the remaining useful life of aging civil infrastructure in the United States has been identified as a major objective in the civil engineering community. Structural health monitoring has emerged as a central tool to fulfill this objective. This paper presents a review of the major structural monitoring programs that have been recently implemented in the United States, focusing on the integrity and performance assessment of large-scale structural systems. Applications where response data from a monitoring program have been used to detect and correct structural deficiencies are highlighted. These applications include (but are not limited to): i) Post-earthquake damage assessment of buildings and bridges; ii) Monitoring of cables vibration in cable-stayed bridges; iii) Evaluation of the effectiveness of technologies for retrofit and seismic protection, such as base isolation systems; and iv) Structural damage assessment of bridges after impact loads resulting from ship collisions. These and many other applications show that a structural health monitoring program is a powerful tool for structural damage and condition assessment, that can be used as part of a comprehensive decision-making process about possible actions that can be undertaken in a large-scale civil infrastructure system after potentially damaging events.

A Proposal to Extend Agility of Wireless Sensor Network using Multi-Agent System for Structural Health Monitoring Application

The rapid and widespread construction of structures calls for rigorous structural health monitoring (SHM) programs throughout the construction, operation and maintenance phases. Thus, much of a structure’s success or failure depends upon efficient SHM programs. The basic technology underlying the SHM program’s ability to detect cracks and failures can be incorporated with Wireless Sensor Network technology for more robust SHM. The agility of the WSN is, therefore, a prominent factor for a successful SHM program. Unpredictable external events, such as earthquakes or serious impacts are an inevitable fact of life. During such an event, the WSN should continue to be active and efficient enough to perform the assigned tasks without failure. The never miss an opportunity scheme (NMAOS) model, which is a prototype based on multi-agent technology, is proposed in this paper to provide agility to the Wireless Sensor Network.

Operational modal testing of historic structures at different levels of excitation

The paper focuses on the dynamic tests of two historic structures, performed in operational conditions at different levels of ambient excitation. The tests were aimed at checking the invariance of the dynamic characteristics (i.e. natural frequencies and mode shapes) with increasing excitation amplitude.A masonry bell tower, dating back to the XVI century, is firstly considered and the modal parameters of key vibration modes were identified from the data collected in two ambient vibration tests: in the first test, the excitation was only provided by micro-tremors and wind, whereas in the second test a higher level of excitation was induced by swinging of bells.The second structure is a well-known iron arch bridge, erected in 1889 and still used as a combined road and rail bridge. Within the dynamic tests performed on the historic infrastructure since June 2009 to address a Structural Health Monitoring program, the bridge response at selected points was continuously recorded for several hours, in order to check the variation of the dynamic characteristics under different traffic conditions.

Validating the Structural Behavior and Response of Burj Khalifa: Synopsis of the Full Scale Structural Health Monitoring Programs

New generation of tall and complex buildings systems are now introduced that are reflective of the latest development in materials, design, sustainability, construction, and IT technologies. While the complexity in design is being overcome by the availability and advances in structural analysis tools and readily advanced software, the design of these buildings are still reliant on minimum code requirements that yet to be validated in full scale. The involvement of the author in the design and construction planning of Burj Khalifa since its inception until its completion prompted the author to conceptually develop an extensive survey and real-time structural health monitoring program to validate all the fundamental assumptions mad for the design and construction planning of the tower. The Burj Khalifa Project is the tallest structure ever built by man; the tower is 828 meters tall and comprises of 162 floors above grade and 3 basement levels. Early integration of aerodynamic shaping and wind engineering played a major role in the architectural massing and design of this multi-use tower, where mitigating and taming the dynamic wind effects was one of the most important design criteria established at the onset of the project design. Understanding the structural and foundation system behaviors of the tower are the key fundamental drivers for the development and execution of a state-of-the-art survey and structural health monitoring (SHM) programs. Therefore, the focus of this paper is to discuss the execution of the survey and real-time structural health monitoring programs to confirm the structural behavioral response of the tower during construction stage and during its service life; the monitoring programs included 1) monitoring the tower's foundation system, 2) monitoring the foundation settlement, 3) measuring the strains of the tower vertical elements, 4) measuring the wall and column vertical shortening due to elastic, shrinkage and creep effects, 5) measuring the lateral displacement of the tower under its own gravity loads (including asymmetrical effects) resulting from immediate elastic and long term creep effects, 6) measuring the building lateral movements and dynamic characteristic in real time during construction, 7) measuring the building displacements, accelerations, dynamic characteristics, and structural behavior in real time under building permanent conditions, 8) and monitoring the Pinnacle dynamic behavior and fatigue characteristics. This extensive SHM program has resulted in extensive insight into the structural response of the tower, allowed control the construction process, allowed for the evaluation of the structural response in effective and immediate manner and it allowed for immediate correlation between the measured and the predicted behavior. The survey and SHM programs developed for Burj Khalifa will with no doubt pioneer the use of new survey techniques and the execution of new SHM program concepts as part of the fundamental design of building structures. Moreover, this survey and SHM programs will be benchmarked as a model for the development of future generation of SHM programs for all critical and essential facilities, however, but with much improved devices and technologies, which are now being considered by the author for another tall and complex building development, that is presently under construction.

Optimization of Bridge Management under Budget Constraints

Bridge management is a complex engineering issue with public safety and financial implications. It is a challenge to provide efficient bridge management systems, taking into account uncertainties in the structural degradation process, the loads and resistance of the structure, the decisions that are made concerning assessment strategies—such as inspections or structural health monitoring (SHM)—and maintenance and rehabilitation strategies. It is important to determine optimal management strategies that satisfy budget constraints, because efficient use of available financial funds is a primary objective of stakeholders. The purpose of this paper is to provide a probabilistic framework based on SHM results for optimization of management strategies under multiple criteria, including budget availability. To take into account uncertainties in the decision process, an event tree–based approach is used. This enables quantification of the expected inspection, SHM, maintenance and failure costs, and accuracy of the decision process according to the occurrence and duration of future SHM programs. To include budget constraints, the probability distribution of management cost—defined as the sum of inspection, SHM, and maintenance costs—is determined at each decision time and compared with an ideal budget distribution. The proposed approach is applied to an existing bridge.

Initial performance of the lower deck of the Centre Street Bridge, Calgary

An initial assessment program was implemented for the lower deck of the Centre Street Bridge in Calgary. The program was designed so that the short-term behaviour of both the glass fibre reinforced polymer (GFRP) and steel reinforcements used in the lower deck, and their respective spans, could be compared. The monitoring provided useful information for verifying GFRP reinforcement design performance, and also raised challenging questions as to how the spans actually behave under thermal loading. Results indicated very little difference in the performances of the spans or their reinforcements over the three year monitoring period. Instrumentation and data acquisition systems are described herein, and representative data are presented. This project allowed the authors to develop some guidance criteria vis-à-vis structural health monitoring (SHM) requirements. During the latter stages of the monitoring program, the lower deck monitoring system was considered for its potential as a long-term SHM program, but on evaluation was found to have features that made it unsuitable for SHM purposes.

Acoustic Emission Testing of Beams to Simulate SHM of Vintage Reinforced Concrete Deck Girder Highway Bridges

The practical application of Acoustic emission (AE) testing in the Structural Health Monitoring (SHM) of vintage steel reinforced concrete highway bridges has been recently investigated by the Oregon Department of Transportation (ODOT) and Oregon State University (OSU). A large number of full scale laboratory test beams designed to simulate a particular class of highway bridge structure and a particular type of structural damage were tested with AE. The results of the AE test data analyzed using the Felicity and Calm ratios are presented and related to physically observed structural damage. New thresholds on the Felicity and Calm ratios specific to this class of structure and loading are suggested (based on the test results) to be used in both the damage assessment diagrams developed in the Japanese Non-destructive Inspection Standard NDIS-2421 and implementation into a SHM program currently under development at ODOT.

Multiresponse Parameter Estimation for Finite-Element Model Updating Using Nondestructive Test Data

Structural health monitoring using field measurements has developed into a major research area, responding to an increasing demand for evaluating the integrity of civil engineering structures. Model updating through parameter estimation is a key tool in a successful structural health monitoring program. A method for parameter estimation is developed for simultaneous use of static and modal nondestructive test data called the “multiresponse” parameter estimation. An error function normalization technique is also developed to facilitate effective multiresponse parameter estimation. This normalization technique can mitigate some of the numerical issues encountered during the parameter estimation procedure. However, this technique does not degrade the integrity of the parameter estimation procedure. Multiresponse parameter estimation provides an increased level of flexibility and feasibility of model updating for structural health monitoring. This paper presents full integration of static and modal nondestructive test data using both stiffness-based and mass-based error functions for structural health monitoring. A benchmark laboratory grid model of a bridge deck is utilized to illustrate application of both normalization and multiresponse parameter estimation for updating the stiffness and mass parameters using nondestructive test data.

８．メインテナンスの力学問題 都市インフラに関する構造ヘルスモニタリングの現状と展望―展望論文―

Structural Health Monitoring (SHM) has been one of the most particular concerns of the civil engineering community worldwide, especially in North America, EU and some Asia countries such as Japan, China andK orea etc. The essence of SHM can be considered to involve measurement, inspection, and assessment ofi n-service structures on a continuous basis with minimum labor requirement. Therefore, the challenging problems we are facing are effective sensing, reliable data gathering, rational data interpretation, correct decision making and accepted cost. In this paper, the basic concept, the needs, and the composition of SHM and basicfl owchart for implementing a SHM program for infrastructure, and the international activities are illustrated and presented first. Then the current-status on developing the element technologies such as sensing technology, data communication and information technologies and modeling feature selection and diagnosis technologies are briefly reviewed. Furthermore, the future research directions of R&D, and design code and standardization for SHM are also discussed.