Yashar Eftekhar Azam

University of New Hampshire | UNH · Department of Civil Engineering

In this research, the variational mode decomposition (VMD) method is used for the drive-by health monitoring of bridges. Firstly, the problem of a half-trailer tractor moving over a bridge is formulated. Next, a Finite Element (FE) code is developed and verified against modal analysis results where complete agreement is found. The vehicle's output...

In this study, an innovative two-dimensional Convolutional Neural Network (2D CNN) architecture is proposed and investigated for the classification of bridge damage. Employing unique strain time-history data transformed into grayscale images, the approach seamlessly combines feature extraction and classification, allowing for the precise identifica...

This study proposes a novel filtering method for unbiased minimum variance state estimation. A key challenge in the development of digital twinning and system identification of operational civil infrastructure is the difficulties associated with measurement of the often nonstationary, and stochastic unknown input to the system, e.g. traffic, earthq...

Structural health monitoring (SHM) is crucial in ensuring the safety and integrity of civil infrastructure. The development and implementation of advanced sensing technologies, machine learning algorithms, and data analytics have revolutionized the field of SHM, making it more predictive, efficient, and effective than ever before. We are pleased...

Crack detection or segmentation on concrete structures is a vital process in structural health monitoring (SHM). Though supervised machine learning techniques have gained tremendous success in this domain, data collection and annotation continue to be challenging. Image data collection is challenging, tedious, and laborious, including accessing rep...

Identifying the mechanical properties of civil structures is required for life-cycle assessment. Kalman filters are exploited for this goal, enabling the online update of a numerical model, acting as the digital twin of the structure, and quantifying the uncertainty of the estimated properties. As uncertainty about model formulation is usually disr...

In this paper, the problem of moving load identification using pure displacement measurements is addressed. It is known that when no assumptions are made on the statistics of the unknown loads and a minimum variance unbiased (MVU) estimation approach is adopted, the existing methods in the literature suffer from a very elevated load estimation unce...

Structural damage detection and prediction under various type of demands has been a significant area of research over the past few decades. Applications of machine (ML) and deep learning (DL) to this topic have provided essential insight into damage detection and prediction in many engineering disciplines, including structural health monitoring (SH...

Structural Health Monitoring (SHM) is essential for ensuring the safety and maintaining the functionality of structures and infrastructure systems, and Machine Learning (ML) techniques have shown great potential in rendering SHM as an automated process. However, unlike many other application areas of ML, when dealing with infrastructure SHM, there...

The effectiveness of Proper Orthogonal Decomposition (POD) for damage identification on out of service, rural bridges is examined using field data. Three similar, out of service, simply supported bridges, consisting of steel beams supporting a concrete deck, were tested prior to demolition and replacement. Strain time histories were recorded using...

Herein, two novel Physics Informed Neural Network (PINN) architectures are proposed for output-only system identification and input estimation of dynamic systems. Using merely sparse output-only measurements, the proposed PINNs architectures furnish a novel approach to input, state, and parameter estimation of linear and nonlinear systems with mult...

Structural damage detection and prediction under various type of demands has been a significant area of research over the past few decades. Applications of machine (ML) and deep learning (DL) to this topic have provided essential insight into damage detection and prediction in many engineering disciplines, including structural health monitoring (SH...

This paper proposes a linear recursive Bayesian filter for minimum variance unbiased joint input and state estimation of structural systems. Unlike the augmented Kalman filter (AKF), the proposed filter falls within the category of Bayesian filters in which unknown inputs are estimated without attributing any fictitious input model or statistics. A...

This study focuses on developing and examining the effectiveness of Transfer Learning (TL) for structural health monitoring (SHM) systems that transfer knowledge about damage states from one structure (i.e., the source domain) to another structure (i.e., the target domain). Transfer Learning (TL) is an efficient method for knowledge transfer and ma...

Railway bridges are subjected to intermittent excitations induced by gravity loads. The monitoring of stress time histories at fatigue prone areas (i.e., “hot spots”) for steel, heavy haul railway bridges is vital for cost-effective maintenance and, most importantly, for maintaining safety. In this regard, strain measurements are crucial for determ...

In recent years the Digital Twin (DT) paradigm has been studied as a futuristic tool for the next generation of infrastructures. Due to the interdisciplinary nature of the design, construction, monitoring, and maintenance of the infrastructures and the cooperation of several stakeholders throughout their lifetime, it is indispensable to introduce a...

In this study, a novel method for online and real-time identification of dynamic systems is presented. This method is based on the newly introduced algorithm Physics Informed Neural Network (PINN). In order to find the dynamic characteristics of the system, sparse displacement measurements are fed to the Artificial Neural Network (ANN); By introduc...

Instantaneous output-only inversion of a system with delayed appearance of input influences on the measured outputs via filtering methods suffer from intensive amplification of the observation noise in the estimated quantities due to the ill-conditionedness. To remedy this issue, in this paper, a new unbiased recursive Bayesian smoothing method is...

This study focused on the development of damage detection indices using unsupervised Machine Learning with data obtained from tests of a full-scale bridge deck mock-up. Measured structural response to different live loads at various damage levels was analyzed and a detection tool, termed a novelty index, was developed. Three levels of damage were i...

Under the cyclic traffic loads, welded structural components of steel bridges may encounter fatigue, which can cause a shorter service life and lead to fracture. A precise fatigue life prediction of structural components requires an accurate collection of stress cycles of the respective component. The density of sensors installed for monitoring the...

This study introduces a novel Bayesian framework for online and real-time vibration control of beam type structures, which represent a comprehensive control system associated with input-state algorithms. Control design systems typically require knowledge of system states, which in structures are displacements and velocities at some degrees of freed...

This report provides a framework for experimental load rating of bridges via inclusion of low-cost dynamic sensors and dynamic tests. Currently 25% of the bridges in Nebraska are posted for live load. According to the National Bridge Inventory (NBI) in 2012, 93% of all postings in the US were based analytical load ratings, 7% were posted using fiel...

Proper orthogonal decomposition (POD) is a useful technique for feature extraction, model order reduction and data compression and has been widely used in different science and engineering disciplines. Numerous papers have been published on the application of offline POD, i.e., batch POD (BPOD) in civil and mechanical engineering encompassing Karhu...

A novel approach to deal with nonlinear system identification of civil structures subjected to unmeasured excitations is presented. Using only sparse global dynamic structural response, mechanics-based nonlinear finite element (FE) model parameters and unmeasured inputs are estimated. Unmeasured inputs are represented by a time-varying autoregressi...

The hybrid simulation method is used to test one or some components of a prototype structure subjected to a plausible loading history, accounting for their interaction with the untested ones, which are simulated numerically. If tested components have similar numerical counterparts, a possible approach to reduce simulation errors is to update the pa...

A novel approach to deal with nonlinear system identification of civil structures subjected to unmeasured excitations is presented. Using only sparse global dynamic structural response, mechanics-based nonlinear finite element (FE) model parameters and unmeasured inputs are estimated. Unmeasured inputs are represented by a time-varying autoregressi...

A novel framework to accurately estimate nonlinear structural model parameters and unknown external inputs (i.e., loads) using sparse sensor networks is proposed and validated. The framework assumes a time-varying auto-regressive (TAR) model for unknown loads and develops a strategy to simultaneously estimate those loads and parameters of the nonli...

Stringer-to-floor beam connections were reported as one of the most fatigue-prone details in riveted steel railway bridges. To detect stiffness degradation that results from the initiation and growth of fatigue cracks, an automated damage detection framework was proposed by the authors (Eftekhar Azam et al., 2019; Rageh et al., 2018). The proposed...

Over the decades, visual inspection has been adopted as a means to monitor infrastructure health. While visual inspection provides insights on a bridge’s condition, it has been generally agreed that it is insufficient and inefficient. This has called for the creation of autonomous, robust, continuous, and quantitative structural health monitoring (...

A successive Bayesian filtering framework for addressing the joint input-state-parameter estimation problem is proposed in this study. Following the notion of analytical, rather than hardware redundancy, the envisaged scheme, (i) adopts realistic assumptions on the sensor network capacity; and (ii) allows for a certain degree of uncertainty in the...

A framework is presented for real-time monitoring of fatigue damage accumulation and prognosis of the remaining lifetime at hotspot locations of new or existing structures by combining output-only vibration measurements from a permanently installed, optimally located, sparse sensor network with the information build into high-fidelity computational...

UNL researchers developed a protocol that would help entities tasked with performing a load rating to determine when a field test would be beneficial in lieu of a traditional, analytical approach or engineering judgment. Decision trees and checklists facilitate selection of an analytical or field-testing approach to produce the ratings. Three trees...

A supervised learning scheme is proposed for detecting, locating, and quantifying the intensity of damage in structures using Artificial Neural Networks (ANNs) and Proper Orthogonal Decomposition (POD). For structural systems, such as buildings and bridges, Proper Orthogonal Modes (POMs) associated with their response are functions of (1) applied e...

An increasing impact of micromechanically governed uncertainties is nowadays foreseen due to the trend of progressively reducing the footprint of MEMS (microelectromechanical systems) devices. For polysilicon MEMS, the two major sources of uncertainties, as resulting from the micro-fabrication process, are linked to the polycrystalline morphology a...

This study presents a new scheme for autonomous health monitoring of railroad infrastructure using a continuous stream of structural health monitoring data. The study utilized measured strains from an optimized sensor set deployed on a double track, steel, railway, truss bridge located in central Nebraska. The most common failure mode for the super...

This paper presents a framework for automated damage detection using a continuous stream of structural health monitoring data. The study utilized measured strains from an optimized sensor set deployed on a double track, steel, railway, truss bridge. Stringer–floor beam connection deterioration, a common deficiency, was the focus of this study; howe...

Microscale uncertainties related to the geometry and morphology of polycrystalline silicon films, constituting the movable structures of micro electro-mechanical systems (MEMS), were investigated through a joint numerical/experimental approach. An on-chip testing device was designed and fabricated to deform a compliant polysilicon beam. In previous...

The structural integrity of buildings and infrastructures can be affected by either environmental conditions or unforeseen external actions. In order to efficiently detect damage, intended as an irreversible degradation of the structural stiffness, many identification algorithms have been proposed in the literature. Nevertheless, a crucial aspect t...

In this paper, an approach based on the synergistic use of proper orthogonal decomposition and Kalman filtering is proposed for the online health monitoring of damaged structures. The reduced-order model of a structure is obtained during an (offline) initial training stage of monitoring; afterward, effective estimations of a possible structural dam...

In this paper, micro-scale uncertainties affecting the behaviour of microelectromechanical systems (MEMS) are investigated through a mixed numerical/experimental approach. An on-chip test device has been designed and fabricated using standard MEMS fabrication techniques, to deform a (microstructured) polysilicon beam. To interpret the experimental...

When the dimensions of polycrystalline structures become comparable to the average grain size, some reliability issues can be reported for the moving parts of inertial microelectromechanical systems (MEMS). Not only the overall behavior of the device turns out to be affected by a large scattering, but also the sensitivity to imperfections gets enha...

A fatigue estimation framework for steel structures is proposed in this study, under realistic assumptions on the sensor network capacity and under the premise of uncertainty in the structural information available throughout the life-cycle of the monitored structure. To this purpose, in a first step, a joint input-state-parameter estimation proble...

In this article, transverse vibration of an Euler-Bernoulli beam carrying a series of traveling masses is analyzed. A semi-analytical approach based on eigenfunction expansion method is employed to achieve the dynamic response of the beam. The inertia of the traveling masses changes the fundamental period of the base beam. Therefore, a comprehensiv...

Health monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main drawback of standard monitoring procedures is lin...

In this study, a novel dual implementation of the Kalman filter proposed by Eftekhar Azam et al. (2014, 2015) is experimentally validated for simultaneous estimation of the states and input of structural systems. By means of numerical simulations, it has been shown that the proposed method outperforms existing techniques in terms of robustness and...

In this work, the problem of fatigue damage prediction in the entire body of metallic structures through sparse output-only vibration measurements is investigated. The use of limited vibration measurements for output-only estimation of fatigue accumulation in structural systems was first proposed in [1]. Online strain estimates for multiple structu...

The influence of the seismic performance of existing bridges on the functionality of communication infrastructures is widely recognized as a crucial issue. Therefore, Hybrid Simulation with Dynamic Substructuring (HS-DS) was selected for assessing the seismic response of a two-pier reinforced concrete (RC) bridge at the Eucentre TREES Laboratory of...

We present here some recent developments on real-time structural health monitoring: 1) optimal deployment of a network of MEMS sensors on flat plates, so as to maximize the system sensitivity to a structural damage; 2) reduced order modeling of the structural behavior through a proper orthogonal decomposition (POD) algorithm, ad-hoc extended to the...

In this study, a novel dual implementation of the Kalman filter proposed by Eftekhar Azam et al. (2014, 2015) is experimentally validated for simultaneous estimation of the states and input of structural systems. By means of numerical simulations, it has been shown that the proposed method outperforms existing techniques in terms of robustness and...

In this work, we propose a new framework for the online detection of damage in plates via vibration measurements. To this end, a finite element model of the plate is handled by a recursive Bayesian filter for simultaneous state and parameter estimation. To drastically reduce the computational costs and enhance the robustness of the filter, such mod...

The subject of predicting structural response, for control or fatigue assessment purposes, via output only vibration measurements is an emerging topic of Structural Health Monitoring. The subject of estimation of the states of a partially observed dynamic system within a stochastic framework has been studied by many scientists and there are well de...

A method for the structural health monitoring (SHM) of compliant, thin plates is discussed. With a specific focus on lightweight composite structures, a proposal for the optimal deployment of a network of surface-mounted inertial micro-sensors (MEMS) is reviewed. Allowing for the measurements gathered through the sensor network as (partial) observa...

In this study, a novel dual implementation of the Kalman filter is proposed for simultaneous estimation of the states and input of structures via acceleration measurements. In practice, the uncertainties stemming from the absence of information on the input force, model inaccuracy and measurement errors render the state estimation a challenging tas...

This paper contributes to the prediction of fatigue damage accumulation in metallic structures that undergo vibrations due to unknown input forces during their operational life. In estimating the strain in a point of interest, the displacement at that point and its vicinity would be needed; therefore, a reliable state estimate could lead to reliabl...

This work aims in the prediction of fatigue damage accumulation in metallic structures subjected to unknown vibrations during their operational life. To accomplish the aforementioned task, knowledge of the time history of the strain in the body of the structure is required. To estimate the strain in a point of interest, the displacement at that poi...

MEMS-based, surface-mounted structural health monitoring systems were recently proposed to locate possible damage events in lightweight composite structures. To track the structural dynamics induced by the external actions and identify in real-time the inception of drifts from the virgin, or undamaged state, recursive Bayesian filters are here adop...

In this article, the resonance of a rectangular plate due to multiple traveling masses is studied. Two series of moving inertial loads traversing the plate surface along parallel rectilinear trajectories with opposite directions are considered. This investigation is of significance in engineering mechanics dealing with the vibration of two-lane sla...

In the current Chapter, recursive Bayesian inference of partially observed dynamical systems is reviewed. As a tool for structural system identification, nonlinear Bayesian filters are applied to dual estimation problem of linear and nonlinear dynamical systems. In so doing, dual estimation of state and parameters of structural state space models i...

In this chapter, the performance of reduced order modeling of dynamic structural systems based on the proper orthogonal decomposition (POD) technique is investigated. Singular value decomposition and principal component analysis of the so-called snapshot matrix are considered to generate the reduced space, onto which the system equations of motion...

This Chapter investigates the statistical properties of residual errors induced by POD-based reduced order modeling. Such errors enter into the state space equations of the reduced systems in terms of system evolution and observation noise. A fundamental assumption made by recursive Bayesian filters, as exploited in this study, is the whiteness of...

In this paper, we investigate the performance of reduced order modeling of dynamic structural systems based on the proper orthogonal decomposition (POD) technique. Singular value decomposition of the so-called snapshot matrix is adopted to generate the reduced space, onto which the system equations of motion are projected to speedup the computation...

In this article, the dynamic responses of a Timoshenko beam subjected to a moving mass, and a moving sprung mass are analyzed. By making recourse to Hamilton’s principle, governing differential equations for beam vibration are derived. By using the modal superposition method, the partial differential equations of the system are transformed into a s...

In this paper, joint identification for structural systems, characterized by severe nonlinearities (softening) in the constitutive model, is pursued via the Sigma-Point Kalman Filter (S-PKF) and the Particle Filter (PF). Since a formal proof of the effects of softening in a stochastic structural system on the accuracy and stability of the filters i...

In this chapter, the dual estimation and reduced order modeling of a damaging structure is studied. In this regard, proper orthogonal decomposition is considered for reduced order modeling in order to find a subspace which optimally captures the dynamics of the system. Through a Galerkin projection, the equations governing the dynamics of the syste...

In this article the constitutive equation of an Euler-Bernoulli beam, excited by multiple moving masses is considered. A set of multiple piezo-ceramic actuators is used to harness the dynamic response of the beam. In this regard the beam response is suppressed by utilizing a linear control algorithm with a time varying gain matrix and displacement-...

In this paper, the sigma-point Kalman filter (S-PKF) is adopted to track the state of composite structures undergoing impact-induced delamination. Estimates provided by the S-PKF are obtained through a set of sigma-points, which independently evolve in time according to the system dynamics. Since the number of sigma-points grows proportionally to t...

The dynamics of a uniaxial micro-accelerometer subjected to accidental drop events is studied by means of a reduced order model. A two degrees of freedom model is built, which carefully reproduces the MEMS response under high acceleration events. The results of the reduced order model are compared to those obtained with a three-dimensional finite e...

Simultaneous state tracking and calibration of constitutive laws for stochastic struc- tural systems is usually pursued via the extended Kalman filter. However, in the pres- ence of severe nonlinearities due to damage inception and growth, filtering may be- come unstable. To improve outcomes, a statistical linearization of the system equa- tions ha...

Sigma-point Kalman filter (S-PKF) has shown promising performances when parameter identification and state tracking are simultaneously pursued in damaging structures. Unlike the extended Kalman Filter, the S-PKF continuously improves the outcomes (i.e. the estimates of state and model parameters) by averaging the responses of a set of independent s...