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Energy harvesting wireless sensor networks are a promising solution for low cost, long lasting civil monitoring applications. But management of energy consumption is a critical concern to ensure these systems provide maximal utility. Many common civil applications of these networks are fundamentally concerned with detecting and analyzing infrequently occurring events. To conserve energy in these situations, a subset of nodes in the network can assume active duty, listening for events of interest, while the remaining nodes enter low power sleep mode to conserve battery. However, judicious planning of the sequence of active node assignments is needed to ensure that as many nodes as possible can be reached upon the detection of an event, and that the system maintains capability in times of low energy harvesting capabilities. In this article, we propose a novel reinforcement learning (RL) agent, which acts as a centralized power manager for this system. We develop a comprehensive simulation environment to emulate the behavior of an energy harvesting sensor network, with consideration of spatially varying energy harvesting capabilities, and wireless connectivity. We then train the proposed RL agent to learn optimal node selection strategies through interaction with the simulation environment. The behavior and performance of these strategies are tested on real unseen solar energy data, to demonstrate the efficacy of the method. The deep RL agent is shown to outperform baseline approaches on both seen and unseen data. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a benchmark problem for the structural health monitoring community to study tall buildings. The benchmark building is called the Green Building located at the Massachusetts Institute of Technology campus, with 21 stories above the ground (83.7 m) and a basement (3.8 m) connecting to the Massachusetts Institute of Technology tunnel system. This building was constructed as cast‐in‐place reinforced concrete and instrumented with 36 accelerometers to measure the building translational, torsional and vertical responses. The benchmark problem includes the detailed description of this building, 7 field measurement data sets (4 ambient data sets, 1 data set under an unidentified event, 1 data set under the excitation of fireworks, and 1 earthquake data set), and finite element models (both full‐scale and condensed models). The Green Building has an identifiable soil‐structure interaction behavior and the base rocking movement brings significant components into the building response. To decouple the rocking effect, storey measurement condensation and rocking response determination are discussed in this paper. A blind source separation approach is finally applied to identify the modal characteristics and quantify the rocking components. The benchmark data and models are open to the public for algorithmic development and validation. [ABSTRACT FROM AUTHOR]

This paper introduces a feature for detecting damage or changes in structures, the continuous symmetry measure, which can quantify the amount of a particular rotational, mirror, or translational symmetry in a mode shape of a structure. Many structures in the built environment have geometries that are either symmetric or almost symmetric, however damage typically occurs in a local manner causing asymmetric changes in the structure's geometry or material properties, and alters its mode shapes. The continuous symmetry measure can quantify these changes in symmetry as a novel indicator of damage for data-based structural health monitoring approaches. This paper describes the concept as a basis for detecting changes in mode shapes and detecting structural damage. Application of the method is demonstrated in various structures with different symmetrical properties: a pipe cross-section with a finite element model and experimental study, the NASA 8-bay truss model, and the simulated IASC-ASCE structural health monitoring benchmark structure. The applicability and limitations of the feature in applying it to structures of varying geometries is discussed. [ABSTRACT FROM AUTHOR]

Fiber reinforced polymer (FRP) is a prevalent material for strengthening or retrofitting concrete structures. It has been found that the effectiveness of entire strengthening or retrofitting scheme highly depends on the bond performance of concrete-epoxy interface. During service life, structures are exposed to complicated and unavoidable mechanical and environmental situations that can cause bond deterioration. In order to ensure the structural safety in a long run, a comprehensive experiment focusing on the coupled effect of sustained load and moisture on the bond property of concrete-epoxy interface is conducted. A drastic deterioration of concrete-epoxy interfacial fracture toughness, up to 77%, is observed under coupled sustained load and moisture exposure. Based on our experimental investigation, a predictive model is developed to describe the bond property variations of concrete-epoxy interface against long-term sustained load and moisture. The proposed predictive model can be used to obtain a more reliable and accurate service life prediction and maintenance planning for FRP-bonded concrete structures. [ABSTRACT FROM AUTHOR]

In this paper, a data-based damage detection algorithm that uses a novel one-class kernel classifier for detection and localisation of damage is presented. The demands of wireless sensing are carefully considered in the development of this fully decentralised and automated methodology. The one-class kernel classifier proposed in this paper is trained through a faster and simpler to implement iterative procedure than other kernel classification methods, while retaining the same advantages over parametric methods, making it especially attractive for embedded damage detection. Acceleration time series at each sensor location are processed into autoregressive and continuous wavelet transform-based damage-sensitive features. Baseline values of these features are used to train the classifier, which can then classify features from new tests as damaged or undamaged, as well as outputting a localisation index, which can be used to identify the location of damage in the structure. This methodology is evaluated using acceleration data taken from a steel-frame laboratory structure under various damage scenarios. A number of parametric studies are also conducted to investigate the effect of sampling frequency and baseline data sample size. [ABSTRACT FROM AUTHOR]

This paper investigates a new probabilistic strategy for Bayesian model updating using incomplete modal data. Direct mode matching between the measured and the predicted modal quantities is not required in the updating process, which is realized through model reduction. A Markov chain Monte Carlo technique with adaptive random-walk steps is proposed to draw the samples for model parameter uncertainty quantification. The iterated improved reduced system technique is employed to update the prediction error as well as to calculate the likelihood function in the sampling process. Since modal quantities are used in the model updating, modal identification is first carried out to extract the natural frequencies and mode shapes through the acceleration measurements of the structural system. The proposed algorithm is finally validated by both numerical and experimental examples: a 10-storey building with synthetic data and a 8-storey building with shaking table test data. Results illustrate that the proposed algorithm is effective and robust for parameter uncertainty quantification in probabilistic model updating of buildings. [ABSTRACT FROM AUTHOR]

This study proposes a new algorithm for damage detection in structures. The algorithm employs an energy-based method to capture linear and nonlinear effects of damage on structural response. For more accurate detection, the proposed algorithm combines multiple damage sensitive features through a distance-based method by using Mahalanobis distance. Hypothesis testing is employed as the statistical data analysis technique for uncertainty quantification associated with damage detection. Both the distance-based and the data analysis methods have been chosen to deal with small size data sets. Finally, the efficacy and robustness of the algorithm are experimentally validated by testing a steel laboratory prototype, and the results show that the proposed method can effectively detect and localize the defects. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Vinylester resin is widely used as matrix for fabricating glass fiber reinforced polymer in marine applications. Even though vinylester based material is understood to be durable in general, the interface between vinylester and the bonded additive can be degraded when subjected to various environmental conditions. Here, the degradation at vinylester/glass interface in chloride environment under elevated temperature is studied through molecular dynamics simulations. The atomistic model consists of a cross-linked vinylester matrix and an amorphous silica substrate. The results show that environmental condition of chloride and elevated temperature leads to the largest loss of interfacial adhesion, which correlates with structural and mechanical degradation of bonded interface. The degradation mechanism is indicated by reduced interfacial stress, decreased vinylester density close to interface, and formation of H-bond. Softened polymer matrix and deteriorated interface inhibit the stress transfer between fiber and matrix, eventually leading to deteriorated macroscopic properties. The performance of degraded vinylester is also compared with other polymer matrixes to provide guidance for designing more durable polymeric composites. This study provides fundamental information on interfacial deterioration in vinylester based composites, which forms the basis for predicting degradation of macroscopic performance. [Display omitted] • An atomistic simulation framework to study the deterioration of vinyl ester based composites is proposed. • Hygrothermal and chloride environment inhibits the stress transfer between fiber and matrix. • Local interfacial deterioration causes the deteriorated fiber/matrix bond under chloride and elevated temperature. • The hydrophilicity of hydroxylated silica surface aggravates the interfacial degradation. • Water-swellable layer of vinyl ester near the fiber surface in chloride environment promotes the interfacial mismatches. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the molecular dynamics (MD) simulation technique is described in the context of structural mechanics applications, providing a fundamental understanding of the atomistic approach, and demonstrating its applicability. Atomistic models provide a bottom-up description of material properties and processes, and MD simulation is capable of solving the dynamic evolution of equilibrium and non-equilibrium processes. The applicability of the technique to structural engineering problems is demonstrated through an interface debonding problem in a multi-layered material system usually encountered in composite structures. Interface debonding may lead to a possible premature failure of fiber reinforced polymer (FRP) bonded reinforced concrete (RC) structural elements subjected to moisture. Existing knowledge on meso-scale fracture mechanics may not fully explain the weakening of the interface between concrete and epoxy, when the interface is under moisture; there is a need to study the moisture affected debonding of the interface using a more fundamental approach that incorporates chemistry in the description of materials. The results of the atomistic modeling presented in this paper show that the adhesive strength (in terms of energy) between epoxy and silica is weakened in the presence of water through its interaction with epoxy. This is correlated with the existing meso-scale experimental data. This example demonstrates that MD simulation can be effectively used in studying the durability of the system through an understanding of how materials interact with the environment at the molecular level. In view of the limitation of MD simulation on both length- and time-scales, future research may focus on the development of a bridging technique between MD and finite element modeling (FEM) to be able to correlate the results from the nano- to the macro-scale. [Copyright &y& Elsevier]

Abstract: Knowledge on durability of concrete/epoxy bonded systems is becoming essential as the use of these systems in applications such as fiber-reinforced polymer (FRP) in strengthening of concrete structures is becoming increasingly popular. Prior research studies in this area indicate that moisture plays an important role on durability of these systems. Premature failures of the bonded system may occur regardless of the durability of the individual constituent materials forming the material system, and that the durability of the overall FRP-bonded system may be governed by the interface integrity. In this study, fracture toughness of concrete/epoxy interfaces as affected by combinations of various degrees of moisture ingress and temperature levels is quantified. For this purpose, sandwiched beam specimens containing concrete/epoxy interfaces were tested and analyzed using the concepts of fracture mechanics. Mechanical properties of individual materials constituting the interface (concrete and epoxy) were also characterized for the evaluation of the corresponding interface fracture toughness. Experimental results have shown a significant decrease, up to about 50%, in the interface fracture toughness of concrete/epoxy bond with selected levels of moisture and temperature conditioning of the specimens for both mode I and mixed mode conditions, and that moisture affected debonding may occur in the interface region involving a distinctive dry-to-wet debonding mode shift from material decohesion (concrete delamination) to interface separation. The mechanistic knowledge and the experimental data presented in this paper will serve as a basis for the use in the design improvement of material systems containing such interfaces for better system durability. [Copyright &y& Elsevier]

Abstract: A radar nondestructive testing (NDT) technique using an airborne horn antenna operating in the far-field condition is developed for detecting damages such as debonding and concrete cracking in glass fiber reinforced polymer (GFRP)-wrapped concrete columns. The far-field airborne radar (FAR) NDT technique is advantageous for distant measurement in practical applications where contact/near-contact measurement becomes an issue. In this technique the radar antenna operates in inverse synthetic aperture radar (ISAR) mode. Laboratory measurements at the frequency range 8–18GHz are made on artificially damaged GFRP–concrete specimens for a preliminary validation of this technique. Collected frequency–angle measurements are further processed by the fast backprojection algorithm to render range–cross-range imagery for damage detection. From the reported measurements and imaging results the proposed FAR NDT technique is conceptually validated; the potential of this technique is shown in identifying defects and debonding in the GFRP–concrete interface regions of the concrete columns wrapped with these composite materials. [Copyright &y& Elsevier]

Abstract: A far-field airborne radar (FAR) nondestructive testing (NDT) technique integrating inverse synthetic aperture radar (ISAR) measurements and a backprojection algorithm for the condition assessment of glass fiber reinforced polymer (GFRP)–wrapped concrete structures is proposed. The method is directed toward the detection of near-surface defects and delaminations located in the vicinity of GFRP–concrete interface. Normal and oblique incidence measurement schemes were adopted and studied for their effectiveness in detecting near-surface anomalies. This technique is also applied to the detection of rebars in reinforced concrete cylinders. Laboratory measurements in the frequency range 8–12GHz were made on artificially damaged GFRP–concrete cylinders for validating the concept of this technique. Spatial resolutions (range and cross-range) are improved by integrating radar measurements at different azimuth angles and in different frequencies, and implementing the developed progressive image focusing scheme. The feasibility of the proposed FAR NDT technique for distant inspection is validated through these studies. [Copyright &y& Elsevier]

Abstract: An integrated methodology for determining the unique combination of complex permittivity based on measured transmission coefficient and time difference of arrival (TDOA) information in free-space measurements is proposed. The methodology consists of an estimation procedure of the real part of complex permittivity based on TDOA, and a root-searching procedure based on parametric system identification (SI) together with an error sum of squares (SSE) criterion. Generally, non-unique combinations of dielectric constant and loss factor are encountered when lossy or low-loss materials are measured and the proposed methodology is aimed at the determination of unique combinations of dielectric constant and loss factor for such materials. The proposed methodology is validated by measurements of several materials with known dielectric properties. The estimated complex permittivity values for Teflon, Lexan, Bakelite, and concrete are in good agreement with those reported in the literature. The method has potential for in-situ measurement of dielectric properties for construction materials. Applicability and limitations of the methodology are discussed. [Copyright &y& Elsevier]

Abstract: A tri-layer interface fracture energy model is developed to facilitate the quantification of various debonding scenarios in fiber reinforced polymer (FRP)/epoxy/concrete plated systems. The work is motivated by the relatively thick adhesive bond line generally encountered in such retrofitted systems. With the explicit material and geometric descriptions and the fairly general boundary conditions, the model provides a basis for parametric study and modeling of debonding in different specimen configurations. Comparison of the derived model specialized for peel and shear debonding with results from finite element modeling demonstrates the robustness of the derived model as an effective means to investigate FRP debonding as a basis for design. [Copyright &y& Elsevier]

Summary: Structural health monitoring (SHM) applications generally utilise high sampling rates, which low‐power wireless protocols are not well equipped to handle. Smart sensing approaches can overcome this, by using the processing capability of the sensor nodes to reduce the volume of data prior to communication. Most smart sensing approaches are preprogrammed and static. This causes two issues: First, the data processing logic cannot be easily modified, making it difficult to update and improve algorithms once deployed. Secondly, there is limited ability to adapt to changes in the environment or degradation of hardware. To address these problems, we have developed a system that allows users to remotely specify their computational logic on the fly in a MapReduce style syntax. We model these user‐specified tasks as a directed acyclic graph, and combine this model with statistics of the performance of each node in the network to formulate an optimisation problem. Solving this problem optimally allocates data processing operations to nodes in the network, such that the total time spent is minimised. We demonstrate a field deployment of this system, which illustrates the advantages of the proposed approach for a typical SHM application, and examines robustness of the system under environmental variations. [ABSTRACT FROM AUTHOR]

Novelty detection is an important task in a variety of applications such as object recognition, defect localization, medical diagnostics, and event detection. The objective of novelty detection is to distinguish one class, for which data are available, from all other possible classes when there is insufficient information to build an explicit model for the latter. The data from the observed class are usually represented in terms of certain features which can be modeled as random variables (RV). An important challenge for novelty detection in multivariate problems is characterizing the statistical dependencies among these RVs. Failure to consider these dependencies may lead to inaccurate predictions, usually in the form of high false positive rates. In this study, we propose conditional classifiers as a new approach for novelty detection that is capable of accounting for statistical dependencies of the relevant RVs without simplifying assumptions. To implement the proposed idea, we use Gaussian mixture models (GMM) along with forward stage-wise additive modeling and boosting methods to learn the conditional densities of RVs that represent our observed data. The resulting model, which is called a boosted conditional GMM, is then used as a basis for classification. To test the performance of the proposed method, we apply it to a realistic application problem for analyzing sensor networks and compare the results with those of alternative schemes. [ABSTRACT FROM AUTHOR]

This work investigates the synergetic effect of nano-silica, NS, (up to 2% by weight) and metakaolin, MK, (up to 9.75% by weight) on the pore- and micro-structure of hardened Ordinary Portland Cement (OPC) pastes after 28 days of curing time. By addition of NS, it was observed that ultra-fine porosity of the cementitious matrix was formed due to the plombierite phase observed via synchrotron XRD analysis. The inclusion of MK along with NS facilitated formation of Calcium-Aluminum-Silicate-Hydrate (C-A-S-H) based crystal structure of wairakite . Mean chain length (MCL) values showed that addition of NS to MK helped in increasing the MCL values up to 7.8, while decreasing the Ca/Si ratio contributed to a higher degree of silicate polymerization that facilitated Calcium-Silicate-Hydrate (C-S-H)/C-A-S-H formation. Water adsorption analysis detected that the inclusion of NS to OPC increased the specific surface area up to 163.5 m 2 /g suggesting probable densification of the cementitious matrix. [ABSTRACT FROM AUTHOR]

Through advances in sensor technology and development of camera-based measurement techniques, it has become affordable to obtain high spatial resolution data from structures. Although measured datasets become more informative by increasing the number of sensors, the spatial dependencies between sensor data are increased at the same time. Therefore, appropriate data analysis techniques are needed to handle the inference problem in presence of these dependencies. In this paper, we propose a novel approach that uses graphical models (GM) for considering the spatial dependencies between sensor measurements in dense sensor networks or arrays to improve damage localization accuracy in structural health monitoring (SHM) application. Because there are always unobserved damaged states in this application, the available information is insufficient for learning the GMs. To overcome this challenge, we propose an approximated model that uses the mutual information between sensor measurements to learn the GMs. The study is backed by experimental validation of the method on two test structures. The first is a three-story two-bay steel model structure that is instrumented by MEMS accelerometers. The second experimental setup consists of a plate structure and a video camera to measure the displacement field of the plate. Our results show that considering the spatial dependencies by the proposed algorithm can significantly improve damage localization accuracy. [ABSTRACT FROM AUTHOR]

This study aimed to measure dynamic responses of structural systems using smartphone videos and vision-based sensing processes. Two algorithms, based on template matching and feature extraction, are employed for this purpose. They were verified on the shake table experiments with single and multi-degree freedom steel specimens where their videos were captured by an ordinary smartphone during excitation. Dynamic responses such as strains at discrete sections as well as displacements, velocities, and accelerations at the floor levels of the specimens were obtained by following the signs of physical or virtual markers during the video recording. Through comprehensive experiments and one available full-scale multi-story shake table experiment in the literature, the developed vision-based algorithms were validated to be used with smartphone videos. It was also shown that regardless of the quality of the video record, substantial characteristics of a specimen or a structure could be determined reasonably by smartphone videos since the absolute mean relative differences varied between 10% to 20%. [ABSTRACT FROM AUTHOR]

In structural health monitoring of composite structures, one important task is to detect and identify the low-velocity impact events, which may cause invisible internal damages. This paper presents a novel approach for simultaneously identifying the impact location and reconstructing the impact force time history acting on a composite structure using dynamic measurements recorded by a sensor network. The proposed approach consists of two parts: (1) an inner loop to reconstruct the impact force time history and (2) an outer loop to search for the impact location. In the inner loop, a newly developed inverse analysis method with Bayesian inference regularization is employed to solve the ill-posed impact force reconstruction problem using a state-space model. In the outer loop, a nonlinear unscented Kalman filter (UKF) method is used to recursively estimate the impact location by minimizing the error between the measurements and the predicted responses. The newly proposed impact load identification approach is illustrated by numerical examples performed on a composite plate. Results have demonstrated the effectiveness and applicability of the proposed approach to impact load identification. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A number of image processing techniques (IPTs) have been implemented for detecting civil infrastructure defects to partially replace human-conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real-world situations (e.g., lighting and shadow changes) can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision-based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions (e.g., strong light spot, shadows, and very thin cracks). Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations. [ABSTRACT FROM AUTHOR]

Calcium-silicate-hydrate (C-S-H) is the main hydration product for ordinary Portland cement ( OPC) materials that exhibits a layered structure containing interfaces that controls the system response to shear deformation at the nanometer scale. In this work, we used molecular statics simulations to study the mechanical behavior of an atomistic model of C-S-H under combined loading conditions that are typical of structural applications of these materials. Combined loading is implemented by first compressing or stretching the atomistic structure to impose an external hydrostatic pressure, and then loading the system through both heterogeneous and homogeneous shear deformation. By utilizing two different shear methodologies, we were able to isolate the interface behavior from the bulk response. Our results show several qualitative similarities with that of macroscale cementitious materials including pressure sensitivity of the maximum shear strength and strength asymmetry in compression and tension. This indicates that the well-known cohesive-frictional behavior of cementitious materials is fundamental to interfaces between C-S-H grains at the nanoscale. Comparing differences in our results with nanoindentation experiments motivate future investigations of the effect of C-S-H particle size and morphology on strength scaling properties at the mesoscale. These mesoscale model interactions should include the normal-stress or pressure dependency that we observe. [ABSTRACT FROM AUTHOR]

Tall buildings are ubiquitous in major cities and house the homes and workplaces of many individuals. However, relatively few studies have been carried out to study the dynamic characteristics of tall buildings based on field measurements. In this paper, the dynamic behavior of the Green Building, a unique 21-story tall structure located on the campus of the Massachusetts Institute of Technology (MIT, Cambridge, MA, USA), was characterized and modeled as a simplified lumped-mass beam model (SLMM), using data from a network of accelerometers. The accelerometer network was used to record structural responses due to ambient vibrations, blast loading, and the October 16th 2012 earthquake near Hollis Center (ME, USA). Spectral and signal coherence analysis of the collected data was used to identify natural frequencies, modes, foundation rocking behavior, and structural asymmetries. A relation between foundation rocking and structural natural frequencies was also found. Natural frequencies and structural acceleration from the field measurements were compared with those predicted by the SLMM which was updated by inverse solving based on advanced multiobjective optimization methods using the measured structural responses and found to have good agreement. [ABSTRACT FROM AUTHOR]

The generalized stacking fault (GSF) is a conceptual procedure historically used to assess shear behavior of defect-free crystalline structures through molecular dynamics or density functional theory simulations. We apply the GSF technique to the spatially and chemically complex quasi-layered structure of calcium-silicate-hydrates (C-S-H), the fundamental nanoscale binder within cementitious materials. A failure plane is enforced to calculate the shear traction-displacement response along a composite interface containing highly confined water molecules, hydroxyl groups, and calcium ions. GSF simulations are compared with affine (homogeneous) shear simulations, which allow strain to localize naturally in response to the local atomic environment. Comparison of strength and deformation behavior for the two loading methods shows the composite interface controls bulk shear deformation. Both models indicate the maximum shear strength of C-S-H exhibits a normal-stress dependency typical of cohesive-frictional materials. These findings suggest the applicability of GSF techniques to inhomogeneous structures and bonding environments, including other layered systems such as biological materials containing organic and inorganic interfaces. [ABSTRACT FROM AUTHOR]

The effect of a high‐performance retarding additive in oil well cements was investigated under elevated temperature (165°C) and pressure (1000 psi) conditions via in situ synchrotron‐based X‐ray diffraction (XRD) and quasielastic neutron scattering (QENS) techniques. Under these temperature and pressure conditions, crystalline calcium silicate hydrates (C–S–H) are formed through the cement hydration process. From in situ XRD experiments, the retardation effect was observed by a change in the rate of the appearance of 11 Å tobermorites as well as a change in the rate of the α‐C2SH generation and depletion. QENS analysis revealed that the retardation effect was related to the non‐conversion of free water to chemical and constrained water components. A high presence of free water components was attributed to a decrease in 11 Å tobermorites along with slower consumption of the quartz and portlandite phases. Furthermore, QENS results infer that the water molecules experienced confinement in the restricted pore spaces. The retarder inhibited this initial water confinement by slowing the bulk diffusion of free water in the confined region. [ABSTRACT FROM AUTHOR]

• A new novelty detection classifier is proposed. • Dependence structure of random variables was considered as the discriminant feature. • Kernel dependence analysis was used to handle arbitrarily high dimensional problems. • No prior information is needed about the dependence structure of random variables. • Over 14% FP reduction compared to gradient boosting and SVM in an SHM problem. In this study, we propose a new approach for novelty detection that uses kernel dependence techniques for characterizing the statistical dependencies of random variables (RV) and use this characterization as a basis for making inference. Considering the statistical dependencies of the RVs in multivariate problems is an important challenge in novelty detection. Ignoring these dependencies, when they are strong, may result in inaccurate inference, usually in the form of high false positive rates. Previously studied methods, such as graphical models or conditional classifiers, mainly use density estimation techniques as their main learning element to characterize the dependencies of the relevant RVs. Therefore, they suffer from the curse of dimensionality which makes them unable to handle high-dimensional problems. The proposed method, however, avoids using density estimation methods, and rather, employs a kernel method, which is robust with respect to dimensionality, to encode the dependencies and hence, it can handle problems with arbitrarily high-dimensional data. Furthermore, the proposed method does not need any prior information about the dependence structure of the RVs; thus, it is applicable to general novelty detection problems with no simplifying assumption. To test the performance of the proposed method, we apply it to realistic application problems for analyzing sensor networks and compare the results to those obtained by peer methods. [ABSTRACT FROM AUTHOR]

• A deep long short-term memory (LSTM) network is developed for nonlinear structural response modeling. • Two input-output schemes (LSTM-s and LSTM-f) are presented. • The deep learning model is capable of modeling both elastic and inelastic response of buildings. • An unsupervised learning algorithm is used to cluster the seismic inputs for training enhancement. • The approach was successfully verified by both numerical and experimental examples. This paper presents a comprehensive study on developing advanced deep learning approaches for nonlinear structural response modeling and prediction. Two schemes of the long short-term memory (LSTM) network are proposed for data-driven structural seismic response modeling. The proposed deep learning model, trained on available datasets, is capable of accurately predicting both elastic and inelastic response of building structures in a data-driven fashion as opposed to the classical physics-based nonlinear time history analysis using numerical methods. In addition, an unsupervised learning algorithm based on a proposed dynamic K-means clustering approach is established to cluster the seismic inputs in order to (1) generate the least but the most informative datasets for training the LSTM and (2) improve the prediction accuracy and robustness of the model trained with limited data. The performance of the proposed approach is successfully demonstrated through three proof-of-concept studies that include a nonlinear hysteretic system, a real-world building with field sensing data, and a steel moment resisting frame. The results show that the proposed LSTM network is a promising, reliable and computationally efficient approach for nonlinear structural response prediction, and offers significant potential in seismic fragility analysis of buildings for reliability assessment. [ABSTRACT FROM AUTHOR]

Early-age hydration kinetics of Portland cement with nano-additives such as nano-silica (NS) was examined using quasielastic neutron scattering (QENS). Cement pastes with different ratios of Portland cement to NS were prepared. The concentration of the NS played a major role in controlling the free and bound water during the hydration of the cement paste. Additionally, the effects of metakaolin (MK) with NS in Portland cements revealed that MK acts as a retarder by decreasing the bounding water capacity during the early age of hydration. An increase in the concentration of NS affected the degree of hydration by reducing the amount of free and mobile water in the gel pores when compared to Portland cement paste. Here, we show that the concentration of NS governs the early-age hydration process in Portland cements. [ABSTRACT FROM AUTHOR]

In recent years, the construction of tall buildings has been increasing in many countries, including Kuwait and other Gulf states. These tall buildings are especially sensitive to ground shaking due to long period seismic surface waves. Although Kuwait is relatively aseismic, it has been affected by large (Mw > 6) regional earthquakes in the Zagros Fold-Thrust Belt (ZFTB). Accurate ground motion prediction for large earthquakes is important to assess the seismic hazard to tall buildings. In this study, we first analyze the observed ground motions due to two earthquakes widely felt in Kuwait: the 08/18/2014 Mw 6.2 earthquake, 360 km NNE of Kuwait City, and the 11/12/2017 Mw 7.3 earthquake, 642 km NNE of Kuwait City. The peak spectral displacement periods of the ground motion from the 08/18/2014 Mw 6.2 earthquake matched well with the ambient vibration spectra of the tallest building—the Al-Hamra Tower. We calculate the ground motions from potential regional and local earthquakes. We use a velocity model obtained by matching the observed seismograms of the 2014 and 2017 earthquakes. We calculate ground motions in Kuwait due to a regional Mw = 7.5 earthquake, and a local Mw = 5.0 earthquakes. Our study shows that a significant source of seismic hazard to tall buildings in Kuwait comes from the regional tectonic earthquakes. However, local earthquakes have the potential to generate high peak ground accelerations (~ 98 cm/s2) close to their epicenters. [ABSTRACT FROM AUTHOR]

Abstract: Computer vision‐based techniques were developed to overcome the limitations of visual inspection by trained human resources and to detect structural damage in images remotely, but most methods detect only specific types of damage, such as concrete or steel cracks. To provide quasi real‐time simultaneous detection of multiple types of damages, a Faster Region‐based Convolutional Neural Network (Faster R‐CNN)‐based structural visual inspection method is proposed. To realize this, a database including 2,366 images (with 500 × 375 pixels) labeled for five types of damages—concrete crack, steel corrosion with two levels (medium and high), bolt corrosion, and steel delamination—is developed. Then, the architecture of the Faster R‐CNN is modified, trained, validated, and tested using this database. Results show 90.6%, 83.4%, 82.1%, 98.1%, and 84.7% average precision (AP) ratings for the five damage types, respectively, with a mean AP of 87.8%. The robustness of the trained Faster R‐CNN is evaluated and demonstrated using 11 new 6,000 × 4,000‐pixel images taken of different structures. Its performance is also compared to that of the traditional CNN‐based method. Considering that the proposed method provides a remarkably fast test speed (0.03 seconds per image with 500 × 375 resolution), a framework for quasi real‐time damage detection on video using the trained networks is developed. [ABSTRACT FROM AUTHOR]

This work studies the effect on Embodied Energy (EE) of concrete when Ordinary Portland Cement (OPC) is partially substituted with natural Pozzolanic Volcanic Ash (VA) at the material and the building scale. The work aims to demonstrate potential improvements to the EE of buildings by comparing the EE of the cement mix with VA replacement to that of baseline case of traditional concrete. Embodied Energy Coefficients (EEC) express the EE of each building product in Mega Joules (MJ) per kg of material. Hardened cement paste made with up to 50% of the OPC replaced by volcanic ash with a mean particle size of either 17 μm or 6 μm is considered. Replacement of OPC with volcanic ash decreases the EEC, however the mix design must be engineered considering the volcanic ash composition to maintain the optimum mechanical strength. Grinding the volcanic ash from 17 μm to 6 μm led to increased compressive strength when replacing up to 40% of OPC with 6 μm sized volcanic ash. An average of 16% decrease in EEC values can be achieved when 40% OPC was replaced with VA. On a building scale, the initial EE is the energy consumed related to the extraction, production, and transportation of materials. For buildings with an average Structural Material Quantities (SMQ, expressed in mass of material per area) value of approximately 2000 kg/m 2 , a 16% decrease in EE value was observed among a sample set of 26 residential and commercial buildings when 50% of OPC is replaced with VA. The demonstrated reduction in EE values were calculated when natural supplementary cementitious materials (SCM) such as volcanic ash are used as a partial replacement to OPC, and it can be adapted to design and build energy-efficient systems tailored for structural and non-structural applications. [ABSTRACT FROM AUTHOR]

This study investigates the early ages of hydration behavior when basaltic volcanic ash was used as a partial substitute to ordinary Portland cement using ultra-small-angle X-ray scattering and wide-angle X-ray scattering (WAXS). The mix design consisted of 10, 30 and 50% substitution of Portland cement with two different-sized volcanic ashes. The data showed that substitution of volcanic ash above 30% results in excess unreacted volcanic ash, rather than additional pozzolanic reactions along longer length scales. WAXS studies revealed that addition of finely ground volcanic ash facilitated calcium-silicate-hydrate related phases, whereas inclusion of coarser volcanic ash caused domination by calcium-aluminum-silicate-hydrate and unreacted MgO phases, suggesting some volcanic ash remained unreacted throughout the hydration process. Addition of more than 30% volcanic ash leads to coarser morphology along with decreased surface area and higher intensity of scattering at early-age hydration. This suggests an abrupt dissolution indicated by changes in surface area due to the retarding gel formation that can have implication on early-age setting influencing the mechanical properties of the resulting cementitious matrix. The findings from this work show that the concentration of volcanic ash influences the specific surface area and morphology of hydration products during the early age of hydration. Hence, natural pozzolanic volcanic ashes can be a viable substitute to Portland cement by providing environmental benefits in terms of lower-carbon footprint along with long-term durability. [ABSTRACT FROM AUTHOR]

This work investigates the effectiveness of the use of volcanic ash along with silica fume as a partial replacement for Portland cement. Multiple mix combinations of volcanic ash, silica fume and Portland cement were examined using various pore and microstructure characterization techniques. Hardened cement pastes were cured for 28 days and their pore and microstructures were examined using X-ray Microtomography, Magic Angle Spinning (MAS) Nuclear magnetic resonance (NMR) for 27 Al and 29 Si, X-ray Diffraction (XRD) and Fourier transform infra-red spectroscopy (FTIR). Microstructure examination of mixes prepared with volcanic ash, silica fume and Portland cement revealed the co-existance of calcium silicate hydrate (C-S-H) and calcium–alumino-silicate-hydrate (C-A-S-H) gels, along with other hydration products that led to a reduction in porosity and densification of the cement matrix. These findings indicate that volcanic ash along with silica fume is a viable substitute for Portland cement up to 40% and provides a sustainable, cost effective and environmentally friendly solution to volcanic ash disposal. [ABSTRACT FROM AUTHOR]

Concrete production contributes heavily to greenhouse gas emissions, thus a need exists for the development of durable and sustainable concrete with a lower carbon footprint. This can be achieved when cement is partially replaced with another material, such as waste plastic, though normally with a tradeoff in compressive strength. This study discusses progress toward a high/medium strength concrete with a dense, cementitious matrix that contains an irradiated plastic additive, recovering the compressive strength while displacing concrete with waste materials to reduce greenhouse gas generation. Compressive strength tests showed that the addition of high dose (100 kGy) irradiated plastic in multiple concretes resulted in increased compressive strength as compared to samples containing regular, non-irradiated plastic. This suggests that irradiating plastic at a high dose is a viable potential solution for regaining some of the strength that is lost when plastic is added to cement paste. X-ray Diffraction (XRD), Backscattered Electron Microscopy (BSE), and X-ray microtomography explain the mechanisms for strength retention when using irradiated plastic as a filler for cement paste. By partially replacing Portland cement with a recycled waste plastic, this design may have a potential to contribute to reduced carbon emissions when scaled to the level of mass concrete production. [ABSTRACT FROM AUTHOR]

The problem of automatic damage detection in civil structures is complex and requires a system that can interpret collected sensor data into meaningful information. We apply our recently developed switching Bayesian model for dependency analysis to the problems of damage detection and classification. The model relies on a state-space approach that accounts for noisy measurement processes and missing data, which also infers the statistical temporal dependency between measurement locations signifying the potential flow of information within the structure. A Gibbs sampling algorithm is used to simultaneously infer the latent states, parameters of the state dynamics, the dependence graph, and any changes in behavior. By employing a fully Bayesian approach, we are able to characterize uncertainty in these variables via their posterior distribution and provide probabilistic estimates of the occurrence of damage or a specific damage scenario. We also implement a single class classification method which is more realistic for most real world situations where training data for a damaged structure is not available. We demonstrate the methodology with experimental test data from a laboratory model structure and accelerometer data from a real world structure during different environmental and excitation conditions. [ABSTRACT FROM AUTHOR]

Although the human visual system is remarkable at perceiving and interpreting motions, it has limited sensitivity and we cannot see motions that are smaller than some threshold. Although difficult to visualize, tiny motions below this threshold are important and can reveal physical mechanisms, or be precursors to large motions in the case of mechanical failure. Here, we present a "motion microscope," a computational tool that quantifies tiny motions in videos and then visualizes them by producing a new video in which the motions are made large enough to see. Three scientific visualizations are shown, spanning macroscopic to nanoscopic length scales. They are the resonant vibrations of a bridge demonstrating simultaneous spatial and temporal modal analysis, micrometer vibrations of a metamaterial demonstrating wave propagation through an elastic matrix with embedded resonating units, and nanometer motions of an extracellular tissue found in the inner ear demonstrating a mechanism of frequency separation in hearing. In these instances, the motion microscope uncovers hidden dynamics over a variety of length scales, leading to the discovery of previously unknown phenomena. [ABSTRACT FROM AUTHOR]

Raman Spectroscopy was used to track the free water and the cementitious gel phases when Ordinary Portland Cement (OPC) and tricalcium silicate (C 3 S) were partially substituted by natural pozzolanic volcanic ash. Two different particle sizes of volcanic ash (VA), (namely 17 and 6 μm) were each used to substitute 10%, 30% and 50% of OPC and C 3 S by weight. Particle size and concentration of VA significantly affected the hydration behavior of cement paste by influencing the water dynamics during the course of hydration. Increase in concentration of VA also led to an increase in “free water” during the course of hydration. Calcium silicate hydrate (C-S-H) phases along with calcium/magnesium based zeolites were detected as the concentration of volcanic ash increased in the mix. This work shows that Raman spectroscopy is a powerful tool to examine the hydration process when Portland cements are substituted by volcanic ashes of different sizes. [ABSTRACT FROM AUTHOR]

Continuous monitoring of engineering structures provides a crucial alternative to assess its health condition as well as evaluate its safety throughout the whole service life. To link the field measurements to the characteristics of a building, one option is to characterize and update a model, against the measured data, so that it can best describe the behavior and performance of the structure. In this paper, we present a novel computational strategy for Bayesian probabilistic updating of building models with response functions extracted from ambient noise measurements using seismic interferometry. The intrinsic building impulse response functions (IRFs) can be extracted from ambient excitation by deconvolving the motion recorded at different floors with respect to the measured ambient ground motion. The IRF represents the representative building response to an input delta function at the ground floor. The measurements are firstly divided into multiple windows for deconvolution and the IRFs for each window are then averaged to represent the overall building IRFs. A hierarchical Bayesian framework with Laplace priors is proposed for updating the finite element model. A Markov chain Monte Carlo technique with adaptive random-walk steps is employed to sample the model parameters for uncertainty quantification. An illustrative example is studied to validate the effectiveness of the proposed algorithm for temporal monitoring and probabilistic model updating of buildings. The structure considered in this paper is a 21-storey concrete building instrumented with 36 accelerometers at the MIT campus. The methodology described here allows for continuous temporal health monitoring, robust model updating as well as post-earthquake damage detection of buildings. [ABSTRACT FROM AUTHOR]

Early age hydration kinetics of Portland cement with pozzolanic volcanic ash was examined using quasielastic neutron scattering. Volcanic ash consisting of two different particle sizes was used to prepare cement pastes with different ratios of Portland cement to volcanic ash. The concentration of the volcanic ash played a major role in the bound water index and self-diffusion coefficients of hydration water confined in the cement paste. An increase in the particle size of the volcanic ash affected the degree of hydration by allowing more free and mobile water in the gel pores, suggesting that volcanic ash may not have completely reacted during the experimental time frame. This study shows that the particle size along with variation in volcanic ash composition governs the early age hydration process in volcanic ash cements. [ABSTRACT FROM AUTHOR]

In recent years, continuum and atomistic modeling of cementitious materials has provided significant advances towards studying the durability of civil infrastructure. An important frontier to understanding structure-property relationships is the “mesoscale”, which represents the bridge between underlying (e.g. molecular) processes and bulk macroscale behavior. This review highlights examples of a mesoscale approach within biological materials and emphasizes their applicability to the study and design of sustainable cement-based materials at multiple length scales. We propose a methodology focused on the coupling of computation and experiment for furthering our understanding of the microstructural properties that control the durability of hardened cement paste. [ABSTRACT FROM AUTHOR]

The use of volcanic ash as a partial substitute to Portland cement can be a viable alternative for producing sustainable and durable cementitious materials. This study investigates the effect of early and late age curing of hardened cement pastes made with volcanic ash and Ordinary Portland Cement (OPC). Pore and microstructure studies were performed on hardened cement pastes prepared with 10% incremental substitution of volcanic ash up to 50% substitution of OPC. Densification in hardened cement pastes was attributed to formation of Calcium Silicate Hydrate (C-S-H) and Calcium-Alumino-Silicate-Hydrate (C-A-S-H) gels, while the development of Magnesium-Silicate-Hydrates (M-S-H) led to decalcification of C-S-H and C-A-S-H gels which resulted in an increase in porosity of the cementitious matrix. A combination of bulk and surface characterization techniques was used to facilitate effective usage of volcanic ash as a potential substitute for Portland cement that provides a sustainable construction material, and environmentally friendly solution to volcanic ash disposal. [ABSTRACT FROM AUTHOR]

Accurate measurement of interfacial properties is critical any time two materials are bonded--in composites, tooth crowns, or when biomaterials are attached to the human body. Yet, in spite of this importance, reliable methods to measure interfacial properties between dissimilar materials remain elusive. Here we present an experimental approach to quantify the interfacial fracture energy Γi, that also provides unique mechanistic insight into the interfacial debonding mechanism at the nanoscale. This approach involves deposition of an additional chromium layer (superlayer) onto a bonded system, where interface debonding is initiated by the residual tensile stress in the superlayer, and where the interface can be separated in a controlled manner and captured in situ. Contrary to earlier methods, our approach allows the entire bonded system to remain in an elastic range during the debonding process, such that Γi can be measured accurately. We validate the method by showing that moisture has a degrading effect on the bonding between epoxy and silica, a technologically important interface. Combining in situ through scanning electron microscope images with molecular simulation, we find that the interfacial debonding mechanism is hierarchical in nature, which is initiated by the detachment of polymer chains, and that the three-dimensional covalent network of the epoxy-based polymer may directly influence water accumulation, leading to the reduction of Γi under presence of moisture. The results may enable us to design more durable concrete composites that could be used to innovate transportation systems, create more durable buildings and bridges, and build resilient infrastructure. [ABSTRACT FROM AUTHOR]

Abstract: Fiber reinforced polymer (FRP) materials have been increasingly used in the last two decades to improve various structural characteristics of reinforced concrete (RC) bridges, buildings and other structures. Ductility of the resulting FRP–concrete system plays an important role in structural performance, especially in certain applications such as earthquake resistant design of structures, where ductility and energy dissipation play a vital role. Wrapping RC columns with FRP has been shown to generally result in significant increase in ductility due to the confinement of concrete by the FRP. Other applications such as flexural strengthening of beams involve tradeoffs between ductility and the desired load capacity. Furthermore, environmental factors may adversely affect the FRP–concrete bond raising concerns about the ductility of the system due to possible premature failure modes. Characterization of these effects requires the use of more involved mechanics concepts other than the simple elastic or ultimate strength analyses. This paper focuses on characterizing ductility of the FRP–concrete systems at different length scales using a combined experimental/computational mechanics approach. Effects of several parameters on ductility, including constituent material properties and their interfaces, FRP reinforcement geometry at the macro- and meso-level, and atomistic structure at the molecular level are discussed. Integration of this knowledge will provide the basis for improved design strategies considering the ductility of FRP–concrete systems from a global as well as local perspective including interface bond behavior under various mechanical and environmental conditions. [Copyright &y& Elsevier]

• Energy-based methodology in seismic resistant design for RC frames is proposed. • Proposed energy-balance method is coupled with performance-based seismic design. • Constant amplitude fatigue is used in energy dissipation capacities of RC members. • Energy demand distribution in RC frame is done by nonlinear static pushover analysis. • Numerical examples include code-compliant design and soft-story frames. The force- and displacement-based design methodologies have been widely used in current seismic design practice. In both methodologies, inelastic behavior is determined indirectly since the seismic demands are derived from elastic acceleration response spectra. In this study, a novel energy-based design (EBD) approach is proposed in the determination of both seismic demand and dissipation capacities of reinforced concrete (RC) frame members through their inelastic behavior directly. In the case of frame-type RC structures, the distribution of demand plastic energy throughout the structure is achieved. The proposed EBD is then accomplished by comparing the demand plastic energy with the energy dissipation capacity of members while integrating performance-based design guidelines. A cantilever column and a benchmark frame, both from the literature, are analyzed in verification of the proposed methodology. Comparisons are made between force, displacement and EBD methodologies in terms of soil condition, member, system and ductility performance requirements. Member and global performance targets are found to be comparable in displacement and EBD methodologies. However, owing to lack of ductility considerations in the prior design methodologies, the proposed EBD showed more reliable result in the determination of both cross-sectional geometry and its rebar configuration. The proposed methodology provides better understanding of damage state limits of RC members by incorporating the plastic energy spectrum, which includes cyclic action, duration, and frequency content of ground motions, as well as inherent ductility in both demand and capacity determinations. The sensitivity analyses between code compliant-design and soft-story-mechanism buildings showed a distinct difference in the variations of seismic energy among the members and their corresponding damage distributions. EBD methodology provides better in depth understanding of the seismic design in terms of demand and capacity of member and overall structural system, and their corresponding performance. [ABSTRACT FROM AUTHOR]

Corrosion in steel pipelines is a common problem that happens due to environmental conditions over time which, if left unchecked, can lead to catastrophic failures of the system. This study involves a multi-scale assessment of corroded pipe sections with diverse characterization modalities at multiple length scales using techniques such as motion magnification with high-speed video, X-ray diffraction, and nanoindentation to bridge the gap between shifts in vibration behavior, and changes in the steel chemical structure, nano-structure, and material properties. Here, vibration monitoring technique coupled with chemical characterization is used to detect and assess the extent of corrosion damage in pipelines. Image 1 [ABSTRACT FROM AUTHOR]

• Small angle neutron scattering and inelastic neutron scattering investigation of cement paste. • Engineering volcanic ash in Portland cement using fractal insights and water dynamics. • Concentration, along with the particle size of the volcanic ash, influences the initiation of gelation in cement paste. Hydration behavior of cements with additives is complicated due to the multifarious nature of hydration products formed at various stages of hydration. Neutron-based scattering techniques are useful in detecting both the microstructural evolution and water dynamics of hydrating cement pastes. This work investigates the effect of early-age hydration on microstructural evolution of cement paste with local volcanic ash using neutron-based beamline techniques. Early-age hydration dynamics of Portland cement paste with volcanic ash was examined via inelastic neutron scattering (INS), while the evolution of microstructure was observed with small angle neutron scattering (SANS). The data obtained from the ratio of volume fractal to surface fractal clearly showed that greater than 30% substitution of volcanic ash leads to unreacted volcanic ash and coarser morphology, which could influence the chemo-mechanical properties of the resulting hydration products. INS results showed that the effect of finer-particle-sized volcanic ash contributes to initiation of gelation, which accommodates higher conversion of free water to bound water. The multi-scale analysis that combines a time-resolved study of water dynamics along with microstructure is found to provide a basis for effectively utilizing engineered additives for the local cement industry. [ABSTRACT FROM AUTHOR]

Understanding the role of retarder on the chemical nature and molecular architecture of hydrating cement paste is essential for engineering oil well cements with additives. Here, synchrotron X-ray and total neutron scattering with pair distribution function (PDF) analysis were performed in combination with calorimetry and nuclear magnetic resonance (NMR) to examine the retarder effect in hydrating tri-calcium silicate (C 3 S) and Class G oil well cement paste. Primarily, the retarder, Diethylenetriamine pentamethylene phosphonic acid (DTPMP) influenced the hydration by affecting the Ca-O and Ca-Si pair correlation providing evidence of calcium playing a predominant role in the retardation process. Secondary effects related to Calcium-Silicate-Hydrate (C-S-H) nuclei poisoning influencing the suppression of calcium hydroxide precipitation were observed. These findings provide insights into the retardation mechanism of hydrating cement paste influenced by calcium depletion when subjected to phosphonate retarders. [ABSTRACT FROM AUTHOR]

Summary: We present a computational methodology for structural identification and damage detection via linking the concepts of seismic interferometry and Bayesian inference. A deconvolution‐based seismic interferometry approach is employed to obtain the waveforms that represent the impulse response functions with respect to a reference excitation source. Using the deconvolved waveforms, we study the following two different damage detection methods that utilize shear wave velocity variations: the arrival picking method and the stretching method. We show that variations in the shear wave velocities can be used for qualitative damage detection and that velocity reduction is more evident for more severely damaged states. Second, a hierarchical Bayesian inference framework is used to update a finite element model by minimizing the gap between the predicted and the extracted time histories of the impulse response functions. Through comparison of the model parameter distributions of the damaged structure with the updated baseline model, we demonstrate that damage localization and quantification are possible. The performance of the proposed approach is verified through two shake table test structures. Results indicate that the proposed framework is promising for monitoring structural systems, which allows for noninvasive determination of structural parameters. [ABSTRACT FROM AUTHOR]