• Structural Monitoring and Maintenance
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• v.8 no.4
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• pp.361-378
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• 2021

While the pavement rating system is being utilized for periodic road condition assessment in the Eastern Region municipality of Saudi Arabia, the condition assessment is costly, time-consuming, and not comprehensive as only few parts of the road are randomly selected for the assessment. Thus, this study is aimed at developing a condition assessment model for a specific sample of a residential road network in Dammam City based on an individual road and a road network. The model was developed using the Analytical Hierarchy Process (AHP) according to the defect types and their levels of severity. The defects were arranged according to four categories: structure, construction, environmental, and miscellaneous, which was adopted from sewer condition coding systems. The developed model was validated by municipality experts and was adjudged to be acceptable and more economical compared to results from the Eastern region municipality (Saudi Arabia) model. The outcome of this paper can assist with the allocation of the government's budget for maintenance and capital programs across all Saudi municipalities through maintaining road infrastructure assets at the required level of services.

• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.107-127
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• 2022

Structural health monitoring (SHM) has been related to damage identification with either operational loads or other environmental loading playing a significant complimentary role in terms of structural safety. In this study, a non-parametric method of time frequency analysis on the measurement is used to address the time-frequency representation for modal parameter estimation and system damage identification of structure. The method employs the wavelet decomposition of dynamic data by using the modified complex Morlet wavelet with variable central frequency (MCMW+VCF). Through detail discussion on the selection of model parameter in wavelet analysis, the method is applied to study the dynamic response of both steel structure and reinforced concrete frame under white noise excitation as well as earthquake excitation from shaking table test. Application of the method to building earthquake response measurement is also examined. It is shown that by using the spectrogram generated from MCMW+VCF method, with suitable selected model parameter, one can clearly identify the time-varying modal frequency of the reinforced concrete structure under earthquake excitation. Discussions on the advantages and disadvantages of the method through field experiments are also presented.

• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.179-200
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• 2022

This paper proposes a data-driven methodology for online early damage identification under changing environmental conditions. The proposed method relies on two data analysis methods: feature-based method and hybrid principal component analysis (PCA) and kernel PCA to separate damage from environmental influences. First, spectral sub-band features, namely, spectral sub-band centroids (SSCs) and log spectral sub-band energies (LSSEs), are proposed as damage-sensitive features to extract damage information from measured structural responses. Second, hybrid modeling by integrating PCA and kernel PCA is performed on the spectral sub-band feature matrix for data normalization to extract both linear and nonlinear features for nonlinear procedure monitoring. After feature normalization, suppressing environmental effects, the control charts (Hotelling T² and SPE statistics) is implemented to novelty detection and distinguish damage in structures. The hybrid PCA-KPCA technique is compared to KPCA by applying support vector machine (SVM) to evaluate the effectiveness of its performance in detecting damage. The proposed method is verified through numerical and full-scale studies (a Bridge Health Monitoring (BHM) Benchmark Problem and a cable-stayed bridge in China). The results demonstrate that the proposed method can detect the structural damage accurately and reduce false alarms by suppressing the effects and interference of environmental variations.

• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.201-220
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• 2022

Lateral vibrations of footbridges may induce synchronization between pedestrians and structure itself, resulting in amplification of such vibrations, a phenomenon identified by lock-in. However, investigations about accelerations and frequencies of the structural movement that are related to the occurrence of synchronization are still incipient. The aim of this paper is to investigate conditions that could lead to avoidance of synchronization among pedestrians themselves and footbridge, expressed in terms of peak acceleration. The focus is on the low acceleration range, employed in some guidelines as a criterion to avoid synchronization. An experimental campaign was carried out, employing a prototype footbridge that was set into oscillatory motion through a pneumatic exciter controlled by a fuzzy system, with controlled frequency and amplitude. Test subjects were then asked to cross the oscillating structure, and accelerations were simultaneously recorded at the structure and at the subject's waist. Pattern and phase differences between these signals were analysed. The results showed that test subjects tended to keep their walking patterns without synchronization induced by the vibration of the structure, for structural peak acceleration values up to 0.18 m/s², when frequencies of oscillation were around 0.8 to 0.9 Hz. On the other hand, for frequencies of oscillation below 0.7 Hz, structural peak accelerations up to 0.30 m/s² did not induce synchronization.

• Structural Monitoring and Maintenance
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• v.9 no.1
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• pp.81-105
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• 2022

The use of concrete filled steel tube (CFST) column is widely accepted due to its property of high axial load carrying capacity, more ductility and more resistant to earthquake specially using in bridges and high-rise buildings. The initial imperfection (δ) that produces during casting or fixing causes the reduction in load carrying capacity, this is the reason, experimental capacity is always less then theoretical one. In this research, the effect of δ on load carrying capacity and behavior of concrete filled steel tube (CFST) column have been investigated by numerically simulation of large number of models with different δ and other geometric parameters that include length (L), width (B), steel tube thickness (t), f'_c and f_y. Finite element analysis software ANSYS v18 is used to develop model of SCFST column to evaluate strength capacity, buckling and failure pattern of member which is applied during experimental study under cyclic axial loading. After validation of results, 42 models with different parameters are evaluated to develop empirical equation predicting axial load carrying capacity for different value of δ. Results indicate that empirical equation shows the 0 to 9% error for finite element analysis Forty-two models in comparison with ANSYS results, respectively. Empirical equation can be used for predicting the axial capacity of early estimating the axial capacity of SCFT column including 𝛿.

• Structural Monitoring and Maintenance
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• v.9 no.1
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• pp.59-80
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• 2022

In the present study, the objective is to detect the structural damages using the responses obtained from the sensors at the optimal location under uncertainty conditions. Reducing the error rate in damage detection process due to responses' noise is an important goal in this study. In the proposed algorithm for optimal sensor placement, the noise of responses recorded from the sensors is initially reduced using the principal component analysis. Afterward, the optimal sensor placement is obtained by the damage detection equation based sensitivity analysis. The sensors are placed on degrees of freedom corresponding to the minimum error rate in structural damage detection through this procedure. The efficiency of the proposed method is studied on a truss bridge, a space dome, a double-layer grid as well as a three-story experimental frame structure and the results are compared. Moreover, the performance of the suggested method is compared with three other algorithms of Average Driving Point Residue (ADPR), Effective Independence (EI) method, and a mass weighting version of EI. In the examples, young's modulus, density, and cross-sectional areas of the elements are considered as uncertainty parameters. Ultimately, the results have demonstrated that the presented algorithm under uncertainty conditions represents a high accuracy to obtain the optimal sensor placement in the structures.

• Structural Monitoring and Maintenance
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• v.9 no.1
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• pp.1-27
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• 2022

Extensive laboratory tests were conducted to investigate the effect of load amplitude, geogrid position, and number of geogrid layers, thickness of ballast layer and clay stiffness on behavior of reinforced ballast layer and induced strains in geogrid. A half full-scale railway was constructed for carrying out the tests, the model consists of two rails 800 mm in length with three wooden sleepers (900 mm × 10 mm × 10 mm). The ballast was overlying 500 mm thickness clay in two states, soft and stiff state. Laboratory tests were conducted to investigate the response of the ballast and the clay layers where the ballast was reinforced by a geogrid. Settlement in ballast and clay, soil pressure and pore water pressure induced in the clay were measured in reinforced and unreinforced ballast cases. It was concluded that the effect of frequency on the settlement ratio is almost constant after 500 cycles. This is due to that the total settlement after 500 cycles, almost reached its peak value, which means that the ballast particles become very close to each other, so the frequency is less effective for high contact particles forces. The average maximum vertical stress and pore water pressure increased with frequency.

• Structural Monitoring and Maintenance
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• v.9 no.4
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• pp.305-321
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• 2022

Bridge structural health monitoring with the aim of continuously assessing structural safety and reliability represents a topic of major importance for worldwide infrastructure managers. In the last two decades, due to their potential economic and operational advantages, drive-by approaches experienced growing consideration from researcher and engineers. This work addresses two technical topics regarding indirect frequency estimation methods: bridge and vehicle dynamics overlapping, and bridge expansion joints impact. The experimental campaign was conducted on a mixed multi-span bridge located in Lombardy using a Ford Galaxy instrumented with a mesh of wireless accelerometers. The onboard time series were acquired for a number of 10 passages over the bridge,performed at a travelling speed of 30 km/h, with no limitations imposed to traffic. Exploiting an ad-hoc sensors positioning, pitch vehicle motion was compensated, allowing to estimate the first two bridge bending frequencies from PSD functions; moreover, the herein adopted approach proved to be insensitive to joints disturbance. Conclusively, a sensitivity study has been conducted to trace the relationship between estimation accuracy and number of trips considered in the analysis. Promising results were found, pointing out a clear positive correlation especially for the first bending frequency.

• Structural Monitoring and Maintenance
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• v.9 no.4
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• pp.337-357
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• 2022

The objective of this study is to present a data-driven machine learning (ML) framework for predicting ultimate shear strength and failure modes of reinforced concrete ledge beams. Experimental tests were collected on these beams with different loading, geometric and material properties. The database was analyzed using different ML algorithms including decision trees, discriminant analysis, support vector machine, logistic regression, nearest neighbors, naïve bayes, ensemble and artificial neural networks to identify the governing and critical parameters of reinforced concrete ledge beams. The results showed that ML framework can effectively identify the failure mode of these beams either web shear failure, flexural failure or ledge failure. ML framework can also derive equations for predicting the ultimate shear strength for each failure mode. A comparison of the ultimate shear strength of ledge failure was conducted between the experimental results and the results from the proposed equations and the design equations used by international codes. These comparisons indicated that the proposed ML equations predict the ultimate shear strength of reinforced concrete ledge beams better than the design equations of AASHTO LRFD-2020 or PCI-2020.

• Structural Monitoring and Maintenance
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• v.9 no.4
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• pp.359-371
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• 2022

Bridges are critical to the civil engineering infrastructure network as they facilitate movement of people, the transportation of goods and services. Given the aging of bridge infrastructure, federal officials mandate visual inspections biennially to identify necessary repair actions which are time, cost, and labor-intensive. Additionally, the expansion joints of bridges are rarely monitored due to cost. However, expansion joints are critical as they absorb movement from thermal effects, loadings strains, impact, abutment settlement, and vehicle motion movement. Thus, the need to monitor bridge expansion joints efficiently, at a low cost, and wirelessly is desired. This paper addresses bridge joint monitoring needs to develop a cost-effective, real-time wireless system that can be validated in a full-scale bridge structure. To this end, a wireless expansion joint monitoring was developed using commercial-off-the-shelf (COTS) sensors. An in-service bridge was selected as a testbed to validate the performance of the developed system compared with traditional displacement sensor, LVDT, temperature and humidity sensors. The short-term monitoring campaign with the wireless sensor system with the internet protocol version 6 over the time slotted channel hopping mode of IEEE 802.15.4e (6TiSCH) network showed reliable results, providing high potential of the developed system for effective joint monitoring at a low cost.