Journal of Civil Structural Health Monitoring最新文献

Abstract

Timber–concrete hybrid structural systems are a practical option to provide tall mass timber buildings with a lateral load-resisting system. This paper discusses the dynamic behavior of an 18-story timber–concrete hybrid building based on the vibration properties evaluated by on-site vibration tests. First, microtremor measurements and human-powered excitation tests were carried out and the obtained vibration data were analyzed using a stochastic subspace identification method to derive natural frequencies, damping ratios, and mode shapes. Then, a finite-element (FE) model was developed based on detailed structural design information, and its eigenvalues and eigenvectors were compared with the test results. The vibration test results showed various mode shapes, including in-plane deformation of the floor diaphragm composed of cross-laminated timber (CLT) panels. The damping ratios in all the modes were scattered between 1 and 3%, and no frequency dependency was observed. The modal properties of the FE model agreed well with the test results by considering the additional stiffness of non-structural components. In order to simulate the in-plane deformation of the CLT floor diaphragm, detailed modeling of the connection between each CLT floor panel and the connection between CLT floor panels and concrete cores is recommended. The findings provide practitioners with an insight into dynamic properties and FE modeling methods of tall timber–concrete hybrid buildings.

Pipe roofs are widely used as an effective proactive support measure in the construction of tunnel entrances, shallow-buried and underground excavated tunnels, underground stations, and large-section soft and weak soil structures. However, the stress variation characteristics of pipe roofs exceeding 40 m in length are not yet clear. This paper utilizes numerical simulation methods to conduct a comprehensive analysis of the deformation characteristics of three excavation methods: center cross-diaphragm method (CRD), both-side heading method, and the three-bench excavation method with super-long pipe roofs combined with temporary inverted arches. It specifically compares the deformation control effectiveness and stress variation patterns of pipe roofs of different lengths. The results indicate that the deformation control effectiveness of 40 m and 20 m long pipe roofs is inferior to that of super-long pipe roofs. Within a range of 30 m in front of the tunnel face and 20 m behind it, significant stress variations of the pipe roof are observed. The most influential range is within 10 m in front of the tunnel face and 5 m behind it. It is evident that the overall load-bearing capacity of the super-long pipe roof is higher than that of pipe roofs below 40 m. Furthermore, in this study, a novel approach is adopted by utilizing fiber optic grating testing technology to achieve comprehensive monitoring of the axial forces in super-long large pipe roofs. The measured data strongly corroborate the accuracy of the numerical calculations.

This paper presents damage assessment through Operational Modal Analysis (OMA) and Finite Element (FE) model updating of the bell tower of the church of Castro in Bergamo, Italy. The tower is a 39 m high reinforced concrete structure with hollow cross-section and double-curved shape. The research was dictated by the need to identify the actual damage state of the structure, which was found through visual inspections. Piezoelectric accelerometers were used to record the ambient vibrations in subsequent test setups, using the roving technique for system identification. A detailed FE model was created with shell elements and calibrated to match the system identification results. A simplified beam model was then developed based on the modal analysis results of the detailed model. A sensitivity analysis was performed to identify the most influential model parameters on the modal characteristics of the system. Subsequently, the optimal values of these parameters were determined by an optimisation procedure carried out using a typical global optimization algorithm. The updating results allowed assessment of the actual condition of the bell tower and its seismic vulnerability. Finally, a seismic strengthening solution was recommended.

This work focuses on the dynamic behavior of bridge piers subjected to scour. Here, the paper is divided into two parts. The first part considers the model of bridge pier by assuming a rocking solid partially embedded in a Winkler soil with translational and rotational conditions at its base. Simple geometry and boundary conditions of bridge pier are represented because the aim of this work is to show the feasibility of a new method based on free response analysis for bridge piers subjected to scour in general case. In fact, this physical model coupling solid mechanics for the structure and the continuum mechanics for the soil makes it possible for us to identify experimentally two rocking modes. In that way, the second part shows an experimental campaign in laboratory implemented on reduced pier models embedded in Fontainebleau sand with different geometries and inertias. From frequency decomposition of signals, natural frequencies and shape modes highlighted by the model are identified and compared from experiments. Analytical formulations and experiments show the interest to use vibration-based monitoring for scouring.

Guided ultrasonic wave (GUW) monitoring systems are gaining much attention in pipeline condition monitoring. However, the effects of environmental and operational conditions (EOCs), especially temperature and random noise, degrade damage detection performance. When EOC effects produce greater amplitudes than the reflected waves from small damage cases, the reflected waves remain unidentified. This paper proposes an unsupervised learning-based denoising autoencoder (DAE) to reduce the effect of EOCs in GUW monitoring systems. A DAE decodes high-dimensional data into low-dimensional features and reconstructs the original data from these low-dimensional features. By providing GUW signals at a reference temperature, this structure forces the DAE to learn the essential features hidden within complex data. The proposed DAE undergoes comparative analysis with other popular unsupervised learning algorithms used for EOC compensation in GUW monitoring systems, such as principal component analysis, independent component analysis and deep autoencoder algorithms. EOC compensation performance is evaluated through receiver operating characteristics (ROC). From the numerical model and an experimental model, the GUW database is obtained. All four algorithms showed good damage detection performance using a numerical model; however, in the experimental model, the proposed DAE showed superiority among other methods.

This paper first explores an alternative non-contact method based on computer vision and explainable machine learning (EML) models to identify and predict vehicle overload cost-effectively. First, 1108 sets of data are extracted from traditional contact measurements, non-contact measurements (Optical Character Recognition and thermal imaging), and literature collection to establish a novel and comprehensive database. The missing value imputation and the randomized search are then selected to find the optimal ML model for further analysis. Moreover, two typical theoretical and five ML models are adopted to evaluate the optimal model’s performance. Finally, the sHapley Additive exPlanations (SHAP) is applied to interpret the influence factors of the optimal ML model. The results indicate that the divided length between the tire and the ground is the most significant input feature, followed by the tire’s inflation pressure, the section height of tire, and the radius. Finally, the proposed model has great application potential for enhancing the efficiency of non-contact vehicle weight-in-motion (WIM) weighing.

Percussion-based methods have attracted growing interest in the assessment of bolt looseness. Nevertheless, their suitability for field applications is constrained by the irregularity in manual percussion force. Variabilities in percussion forces can distort the characterization of signals, resulting in an insufficient assessment of bolt looseness. In response to this challenge, the paper introduces a force-adaptive percussion method that utilizes sound phase as a feature, theoretically demonstrating its resilience to percussion force irregularities for the first time. Verification experiments were conducted on a standard beam-column bolted joint. Experimental results showed that phase features of varied percussion signals under identical preload conditions exhibit good consistency, in contrast to the Mel-frequency cepstral coefficients (MFCCs), another prevalent characteristic feature. To assess the effectiveness of the proposed strategy, a residual structure-integrated network was applied for bolt looseness assessment using both phase features and the MFCCs. The results indicated that the model trained with phase features attained higher classification accuracy and superior generalization capability compared to another model trained with MFCCs. These findings substantiated the validity and superiority of the proposed method, indicating its potential to substantially enhance the applicability of field bolt looseness assessment.

Siltation is a significant element that affects the efficiency and safety of water conveyance tunnels. One efficient inspection technique is optical vision inspection carried out by underwater robots. However, efficient processing is required to handle the volume of images that underwater robots collect. Convolutional neural networks (CNNs), have demonstrated considerable promise in computer vision, however it is challenging to implement these models in underwater robots. In this paper, we propose a classification framework for multiple siltation types based on siltation images of water conveyance tunnels using the structure-optimized MobileNet v3, namely SRNet. An underwater robotic image acquisition device is used to acquire the siltation images for training and testing. Out of 6000 images collected from 7 water conveyance tunnels, 4172 are used to train the proposed SRNet network. The remaining 1828 images are used to test it. Furthermore, multiple learning strategies are used to optimize the entire training process. Compared with other deep learning models, the proposed method shows great superiority in terms of recognition results, computational cost and model size. The proposed method effectively weighs model accuracy and complexity and can be used for rapid and accurate identification of siltation in water conveyance tunnel health monitoring.

An important technique for the quantitative analysis of dam deformation state is to establish safety monitoring models using deformation monitoring data. To address the shortcomings of conventional monitoring models, such as difficulty in selecting influencing factors and poor ability to resist the interference of outliers, this paper develops a structural safety monitoring model that can realize adaptive identification of various types of outliers in dam deformation monitoring data. The Bayesian model selection (BMS) method is first introduced to select the explanatory variables with a significant impact on the modeling process. On this basis, robust regression analysis of dam deformation monitoring data is performed by using the least trimmed squares (LTS) estimation. In particular, the recovery of clean data and the regression learning are conducted jointly. Furthermore, the double wedge plot is proposed, a graphical display which indicates outliers and potential level shifts. The engineering example demonstrates that, compared with the widely used multiple linear regression (MLR) model based on least squares (LS) fitting, the robust regression model based on BMS-LTS can not only effectively determine the key influencing factors but also adaptively identify various types of outliers in the regression. This study improves the significance of regression and increases the accuracy of prediction; thus, it has good applicability in anomaly detection of dam monitoring data and quantitative analysis of dam safety behavior.

The assessment of initial support roughness is of utmost importance in ensuring waterproofing and structural safety in tunnel projects. However, existing measurement methods and evaluation systems fall short of meeting the requirements of efficiency, accuracy, and coverage for roughness measurement and acceptance procedures. This paper introduces an automated measurement method for the initial support roughness utilizing terrestrial laser scanning. This approach significantly enhances the efficiency, accuracy, and automation level of measuring roughness for initial support. In addition, the paper proposes evaluation indicators, such as average deviation, root mean square deviation, and three-dimensional chord ratio, to assess the overall and local roughness of initial support. By extending the assessment of roughness from two-dimensional to three-dimensional, this study improves the accuracy, comprehensiveness, and richness of roughness evaluation. Experimental validation confirms the accuracy and applicability of the proposed method. Furthermore, this paper thoroughly examines the effect of different step length values within the detection area on the accuracy and discriminability of the evaluation indicators when assessing the overall roughness of the initial support. Chromatograms are used to visually present and locate roughness in different areas, greatly aiding in the assessment and treatment of surface diseases in initial support.