STR-918: STRUCTURAL MODAL IDENTIFICATION USING AN IMPROVED EMPIRICAL MODE DECOMPOSITION

Empirical mode decomposition (EMD) has shown significant promises in signal decomposition of vibration data of civil engineering structures. Owing to its self-adaptive time-frequency decomposition capability, it is widely used in system identification of both linear and nonlinear structures. Unlike EMD which uses only single sensor, multivariate EMD (MEMD) is recently explored as a modal identification tool utilizing multichannel vibration measurements. In this paper, the performance of MEMD is investigated by integrating with another powerful signal separation technique to undertake modal identification under a wide range of applications. The proposed EMD method is validated using a suite of numerical studies

STR-839: ADDRESSING THE ISSUES OF MODAL IDENTIFICATION USING TENSOR DECOMPOSITION

Modal identification has been an indispensable tool for condition assessment of critical civil infrastructure. Recently several signal processing techniques including time-frequency analysis have shown significant success in addressing wide range of challenges in modal identification of flexible structures. In a parallel development, tensor decomposition is explored as an attractive and versatile system identification tool that can use even a limited number of vibration sensors to estimate the modal parameters under ambient excitations. In this paper, the performance of tensor decomposition is evaluated for modal identification of a building model under a multitude of earthquake excitations

Decentralized Ambient System Identification of Structures

Many of the existing ambient modal identification methods based on vibration data process information centrally to calculate the modal properties. Such methods demand relatively large memory and processing capabilities to interrogate the data. With the recent advances in wireless sensor technology, it is now possible to process information on the sensor itself. The decentralized information so obtained from individual sensors can be combined to estimate the global modal information of the structure. The main objective of this thesis is to present a new class of decentralized algorithms that can address the limitations stated above. The completed work in this regard involves casting the identification problem within the framework of underdetermined blind source separation (BSS). Time-frequency transformations of measurements are carried out, resulting in a sparse representation of the signals. Stationary wavelet packet transform (SWPT) is used as the primary means to obtain a sparse representation in the time-frequency domain. Several partial setups are used to obtain the partial modal information, which are then combined to obtain the global structural mode information. Most BSS methods in the context of modal identification assume that the excitation is white and do not contain narrow band excitation frequencies. However, this assumption is not satisfied in many situations (e.g., pedestrian bridges) when the excitation is a superposition of narrow-band harmonic(s) and broad-band disturbance. Under such conditions, traditional BSS methods yield sources (modes) without any indication as to whether the identified source(s) is a system or an excitation harmonic. In this research, a novel under-determined BSS algorithm is developed involving statistical characterization of the sources which are used to delineate the sources corresponding to external disturbances versus intrinsic modes of the system. Moreover, the issue of computational burden involving an over-complete dictionary of sparse bases is alleviated through a new underdetermined BSS method based on a tensor algebra tool called PARAllel FACtor (PARAFAC) decomposition. At the core of this method, the wavelet packet decomposition coefficients are used to form a covariance tensor, followed by PARAFAC tensor decomposition to separate the modal responses. Finally, the proposed methods are validated using measurements obtained from both wired and wireless sensors on laboratory scale and full scale buildings and bridges

Damage Assessment in Aging Structures using Augmented Reality

Structural Health Monitoring (SHM) is the assessment of bridges and observation of data regarding these bridges over time to monitor their evolution and detect the presence of any possible damages. However, existing methods to perform structural inspections in bridges are high in cost, time-consuming and risky. Inspectors use expensive equipment to reach a certain area of the bridge to inspect it, and at different heights, this can pose a risk to the inspector’s safety. This study aims to find cheaper, faster, and safer ways to perform structural inspections using augmented reality and artificial intelligence. The developed system uses a machine learning model to detect and classify four different types of damage, the system also provides length, area, and perimeter measurements to further assess the severity of the damage

A Systematic Review of Convolutional Neural Network-Based Structural Condition Assessment Techniques

With recent advances in non-contact sensing technology such as cameras, unmanned aerial and ground vehicles, the structural health monitoring (SHM) community has witnessed a prominent growth in deep learning-based condition assessment techniques of structural systems. These deep learning methods rely primarily on convolutional neural networks (CNNs). The CNN networks are trained using a large number of datasets for various types of damage and anomaly detection and post-disaster reconnaissance. The trained networks are then utilized to analyze newer data to detect the type and severity of the damage, enhancing the capabilities of non-contact sensors in developing autonomous SHM systems. In recent years, a broad range of CNN architectures has been developed by researchers to accommodate the extent of lighting and weather conditions, the quality of images, the amount of background and foreground noise, and multiclass damage in the structures. This paper presents a detailed literature review of existing CNN-based techniques in the context of infrastructure monitoring and maintenance. The review is categorized into multiple classes depending on the specific application and development of CNNs applied to data obtained from a wide range of structures. The challenges and limitations of the existing literature are discussed in detail at the end, followed by a brief conclusion on potential future research directions of CNN in structural condition assessment

Modelling and testing of a historic steel suspension footbridge in Ireland

Daly's Bridge is a historic steel suspension footbridge in Ireland, known locally as the 'Shaky Bridge' for its noticeable movement under pedestrian loading. Although there is concern regarding the performance of the structure, testing or modelling has not been carried out to date and inadequate information exists in relation to carrying out such analyses. In this paper, Daly's Bridge is instrumented and tested for the first time and a model of the bridge is established and improved in the process. Apart from ambient vibration, excitation from traversing pedestrians and cyclists is considered. Video analysis of dynamic deflection, a wavelet-packet-based technique using acceleration responses and dynamic measurements from a cheap smartphone accelerometer application are used to identify and compare the natural frequency of the bridge. The work contributes to the evidence base of full-scale measurements from instrumenting and analysing responses of aging pedestrian bridges, highlighting the complexity, challenges, opportunities and limitations related to the varied levels of information available from disparate sources. The study also highlights the need to investigate to what extent cheap sensors can be successfully used as compared to their more expensive and sophisticated counterparts

Performance Evaluation of Blind Modal Identification in Large-Scale Civil Infrastructure

The monitoring and maintenance of existing civil infrastructure has recently received worldwide attention. Several structural health monitoring methods have been developed, including time-, frequency-, and time–frequency domain methods of modal identification and damage detection to estimate the structural and modal parameters of large-scale structures. However, there are several implementation challenges of these modal identification methods, depending on the size of the structures, measurement noise, number of available sensors, and their operational loads. In this paper, two modal identification methods, Second-Order Blind Identification (SOBI) and Time-Varying Filtering Empirical Mode Decomposition (TVF-EMD), are evaluated and compared for large-scale structures including a footbridge and a wind turbine blade with a wide range of dynamic characteristics. The results show that TVF-EMD results in better accuracy in modal identification compared to SOBI for both structures. However, when the number of sensors is equal to or more than the number of target modes of the structure, SOBI results in better computational efficiencies compared to TVF-EMD

Road Condition Monitoring Using Smart Sensing and Artificial Intelligence: A Review

Road condition monitoring (RCM) has been a demanding strategic research area in maintaining a large network of transport infrastructures. With advancements in computer vision and data mining techniques along with high computing resources, several innovative pavement distress evaluation systems have been developed in recent years. The majority of these technologies employ next-generation distributed sensors and vision-based artificial intelligence (AI) methodologies to evaluate, classify and localize pavement distresses using the measured data. This paper presents an exhaustive and systematic literature review of these technologies in RCM that have been published from 2017–2022 by utilizing next-generation sensors, including contact and noncontact measurements. The various methodologies and innovative contributions of the existing literature reviewed in this paper, together with their limitations, promise a futuristic insight for researchers and transport infrastructure owners. The decisive role played by smart sensors and data acquisition platforms, such as smartphones, drones, vehicles integrated with non-intrusive sensors, such as RGB, and thermal cameras, lasers and GPR sensors in the performance of the system are also highlighted. In addition to sensing, a discussion on the prevalent challenges in the development of AI technologies as well as potential areas for further exploration paves the way for an all-inclusive and well-directed futuristic research on RCM

Augmented Reality-Based Real-Time Visualization for Structural Modal Identification

In the era of aging civil infrastructure and growing concerns about rapid structural deterioration due to climate change, the demand for real-time structural health monitoring (SHM) techniques has been predominant worldwide. Traditional SHM methods face challenges, including delays in processing acquired data from large structures, time-intensive dense instrumentation, and visualization of real-time structural information. To address these issues, this paper develops a novel real-time visualization method using Augmented Reality (AR) to enhance vibration-based onsite structural inspections. The proposed approach presents a visualization system designed for real-time fieldwork, enabling detailed multi-sensor analyses within the immersive environment of AR. Leveraging the remote connectivity of the AR device, real-time communication is established with an external database and Python library through a web server, expanding the analytical capabilities of data acquisition, and data processing, such as modal identification, and the resulting visualization of SHM information. The proposed system allows live visualization of time-domain, frequency-domain, and system identification information through AR. This paper provides an overview of the proposed technology and presents the results of a lab-scale experimental model. It is concluded that the proposed approach yields accurate processing of real-time data and visualization of system identification information by highlighting its potential to enhance efficiency and safety in SHM by integrating AR technology with real-world fieldwork

Machine Learning-Assisted Improved Anomaly Detection for Structural Health Monitoring

The importance of civil engineering infrastructure in modern societies has increased lately due to the growth of the global economy. It forges global supply chains facilitating enormous economic activity. The bridges usually form critical links in complex supply chain networks. Structural health monitoring (SHM) of these infrastructures is essential to reduce life-cycle costs, and determine their remaining life using advanced sensing techniques and data fusion methods. However, the data obtained from the SHM systems describing the health condition of the infrastructure systems may contain anomalies (i.e., distortion, drift, bias, outlier, noise etc.). An automated framework is required to accurately classify these anomalies and evaluate the current condition of these systems in a timely and cost-effective manner. In this paper, a recursive and interpretable decision tree framework is proposed to perform multiclass classification of acceleration data collected from a real-life bridge. The decision nodes of the decision tree are random forest classifiers that are invoked recursively after synthetically augmenting the training data before successive iterations until suitable classification performance is obtained. This machine-learning-based classification model evolved from a simplistic decision tree where statistical features are used to perform classification. The feature vectors defined for training the random forest classifiers are calculated using similar statistical features that are easy to interpret, enhancing the interpretability of the classifier models. The proposed framework could classify non-anomalous (i.e., normal) time-series of the test dataset with 98% accuracy