• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.367-392
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• 2020

The specific characteristics of near-field earthquake records can lead to different dynamic responses of bridges compared to far-field records. However, the effect of near-field strong ground motion has often been neglected in the seismic performance assessment of the bridges. Furthermore, damage to horizontally curved multi-frame RC box-girder bridges in the past earthquakes has intensified the potential of seismic vulnerability of these structures due to their distinctive dynamic behavior. Based on the nonlinear time history analyses in OpenSEES, this article, assesses the effects of near-field versus far-field earthquakes on the seismic performance of horizontally curved multi-frame RC box-girder bridges by accounting the vertical component of the earthquake records. Analytical seismic fragility curves have been derived thru considering uncertainties in the earthquake records, material and geometric properties of bridges. The findings indicate that near-field effects reasonably increase the seismic vulnerability in this bridge sub-class. The results pave the way for future regional risk assessments regarding the importance of either including or excluding near-field effects on the seismic performance of horizontally curved bridges.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.295-317
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• 2020

Investigation of the stability of arch dam abutments is one of the most important aspects in the analysis of this type of dams. To this end, the Bakhtiari dam, a doubly curved arch dam having six wedges at each of its abutments, is selected. The seismic safety of dam abutments is studied through time history analysis using the design-based earthquake (DBE) and maximum credible earthquake (MCE) hazard levels. Londe limit equilibrium method is used to calculate the stability of wedges in abutments. The thrust forces are obtained using ABAQUS, and stability of wedges is calculated using the code written within MATLAB. Effects of foundation flexibility, grout curtain performance, vertical component of earthquake, nonlinear behavior of materials, and geometrical nonlinearity on the safety factor of the abutments are scrutinized. The results show that the grout curtain performance is the main affecting factor on the stability of the abutments, while nonlinear behavior of the materials is the least affecting factor amongst others. Also, it is resulted that increasing number of the contraction joints can improve the seismic stability of dam. A cap is observed on the number of joints, above which the safety factor does not change incredibly.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.319-344
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• 2020

Inverse analysis of non-linear reinforced concrete bridge pier using recursive Gaussian filtering for in-situ condition assessment is the main theme of this work. For this purpose, minimum variance unbiased estimation using unscented sigma points is adopted here. The uniqueness of this inverse analysis lies in its approach for strain based updating of engineering demand parameters, where appropriate bound and constrained conditions are introduced to ensure numerical stability and convergence. In this analysis, seismic input is also identified, which is an added advantage for the structures having no dedicated sensors for earthquake measurement. First, the proposed strategy is tested with a simulated example whose hysteretic properties are obtained from the slow-cyclic test of a frame to investigate its efficiency and accuracy. Finally, the experimental test data of a full-scale bridge pier is used to study its in-situ condition in terms of Park & Ang damage index. Overall the study shows the ability of the augmented minimum variance unbiased estimation based recursive time-marching algorithm for non-linear system identification with the aim to estimate the engineering damage parameters that are the fundamental information necessary for any future decision making for retrofitting/rehabilitation.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.345-365
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• 2020

At present, many machine leaning and data mining methods are used for analyzing and predicting structural response characteristics. However, the platform that combines big data analysis methods with online and offline analysis modules has not been used in actual projects. This work is dedicated to developing a multifunctional Hadoop-Spark big data platform for bridges to monitor and evaluate the serviceability based on structural health monitoring system. It realizes rapid processing, analysis and storage of collected health monitoring data. The platform contains offline computing and online analysis modules, using Hadoop-Spark environment. Hadoop provides the overall framework and storage subsystem for big data platform, while Spark is used for online computing. Finally, the big data Hadoop-Spark platform computational performance is verified through several actual analysis tasks. Experiments show the Hadoop-Spark big data platform has good fault tolerance, scalability and online analysis performance. It can meet the daily analysis requirements of 5s/time for one bridge and 40s/time for 100 bridges.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.283-294
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• 2020

In this paper, an adaptive MEMD based modal identification technique for linear time-invariant systems is proposed employing multiple vibration measurements. Traditional empirical mode decomposition (EMD) suffers from mode-mixing during sifting operations to identify intrinsic mode functions (IMF). MEMD performs better in this context as it considers multi-channel data and projects them into a n-dimensional hypercube to evaluate the IMFs. Using this technique, modal parameters of the structural system are identified. It is observed that MEMD has superior performance compared to its traditional counterpart. However, it still suffers from mild mode-mixing in higher modes where the energy contents are low. To avoid this problem, an adaptive filtering scheme is proposed to decompose the interfering modes. The Proposed modified scheme is then applied to vibrations of a reinforced concrete road bridge. Results presented in this study show that the proposed MEMD based approach coupled with the filtering technique can effectively identify the parameters of the dominant modes present in the structural response with a significant level of accuracy.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.289-303
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• 2022

In this study, the impact of assigned pixel labels on the accuracy of crack image identification of steel structures is examined by using an atrous separable convolution neural network (ASCNN). Firstly, images containing fatigue cracks collected from steel structures are classified into four datasets by assigning different pixel labels based on image features. Secondly, the DeepLab v3+ algorithm is used to determine optimal parameters of the ASCNN model by maximizing the average mean-intersection-over-union (mIoU) metric of the datasets. Thirdly, the ASCNN model is trained for various image sizes and hyper-parameters, such as the learning rule, learning rate, and epoch. The optimal parameters of the ASCNN model are determined based on the average mIoU metric. Finally, the trained ASCNN model is evaluated by using 10% untrained images. The result shows that the ASCNN model can segment cracks and other objects in the captured images with an average mIoU of 0.716.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.259-270
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• 2022

This study explores the use of the recently developed "strain-sensing smart skin" (S⁴) method for noncontact strain measurements on cement-based samples. S⁴ sensors are single-wall carbon nanotubes dilutely embedded in thin polymer films. Strains transmitted to the nanotubes cause systematic shifts in their near-infrared fluorescence spectra, which are analyzed to deduce local strain values. It is found that with cement-based materials, this method is hampered by spectral interference from structured near-infrared cement luminescence. However, application of an opaque blocking layer between the specimen surface and the nanotube sensing film enables interference-free strain measurements. Tests were performed on cement, mortar, and concrete specimens with such modified S⁴ coatings. When specimens were subjected to uniaxial compressive stress, the spectral peak separations varied linearly and predictably with induced strain. These results demonstrate that S⁴ is a promising emerging technology for measuring strains down to ca. 30 𝜇𝜀 in concrete structures.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.271-287
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• 2022

This study investigates the bending behavior of a composite concrete slab roof with different methods of externally strengthing using steel plates and carbon fiber reinforced polymer (CFRP) strips. First, the concrete slab model which was reinforced with CFRP strips on the bottom surface of it is validated using experimental data, and then, using numerical modeling, 7 different models of square-shaped composite slab roofs are developed in ABAQUS software using the finite element modeling. Developed models include steel rebar reinforced concrete slab with variable thickness of CFRP and steel plates. Considering the control sample which has no external reinforcement, a set of 8 different reinforcement states has been investigated. Each of these 8 states is examined with 6 different uncertainties in terms of the properties of the materials in the construction of concrete slabs, which make 48 numerical models. In all models loading process is continued until complete failure occurs. The results from numerical investigations showed using the steel plates as an executive method for strengthening, the bending capacity of reinforced concrete slabs is increased in the ultimate bearing capacity of the slab by about 1.69 to 2.48 times. Also using CFRP strips, the increases in ultimate bearing capacity of the slab were about 1.61 to 2.36 times in different models with different material uncertainties.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.237-258
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• 2022

This paper studies detecting and locating loose bolts using nonlinear guided waves. The 3D Finite Element (FE) simulation is used for the prediction of guided waves' interactions with loose bolted joints. The numerical results are verified by experimentally obtained data. The study considers bolted joints consisting of two bolts. It is shown that the guided waves' interaction with surfaces of a loose bolted joint generates Contact Acoustic Nonlinearity (CAN). The study uses CAN for detecting and locating loose bolts. The processed experimentally obtained data show that the CAN is able to successfully detect and locate loose bolted joints. A 3D FE simulation scheme is developed and validated by experimentally obtained data. It is shown that FE can predict the propagation of guided waves in loose bolts and is also able to detect and locate them. Several numerical case studies with various bolt sizes are created and studied using the validated 3D FE simulation approach. It is shown that the FE simulation modeling approach and the signal processing scheme used in the current study are able to detect and locate the loose bolts in imperfect bolted joints. The outcomes of this research can provide better insights into understanding the interaction of guided waves with loose bolts. The results can also enhance the maintenance and repair of imperfect joints using the nonlinear guided waves technique.

• Structural Monitoring and Maintenance
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• v.8 no.4
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.