Journal of Civil Structural Health Monitoring最新文献

Dam health monitoring is an important method for quantitative evaluation of dam safety. After long-term operation, there have missing data in dam monitoring data series inevitably due to the sensor damage or monitoring system failure problem which seriously affects the correctness of dam safety evaluation. The imputation accuracy of missing value is affected by data decomposition, reconstruction, and prediction methods. Therefore, in view of the high-precision imputation model of missing data in dam health monitoring, this paper proposes a data-driven fusion imputation model based on novel mode decomposition and deep learning method. First, the fusion imputation model is constructed based on extreme-point symmetric mode decomposition (ESMD), permutation entropy (PE), and bidirectional gate recurrent unit neural network (BiGRU). The ESMD-PE data preprocessing module can decompose the original data into a series of stable subsequences which can be input into the advanced deep learning BiGRU model to improve the interpolation accuracy. Then, the types of dam missing data and interpolation steps are studied. The engineering example illustrates that the root mean square error of the proposed model is decreased by 55.32% on average compared with four classical imputation models. The ESMD-PE–BiGRU fusion model can effectively simulate the inherent law of dam monitoring data and predict the missing data, which provides complete monitoring data for dam safety analysis.

Crack monitoring is an important task in structural health monitoring. In this study, a procedure is developed to assess the crack width based on the strain curve of distributed fiber optic sensors (DFOS), taking into account of the strain redistribution of the structural substrate after cracking. Fifteen aluminum alloy plates with two or three pre-cut cracks spaced at varying intervals were installed with DFOS and subjected to a tensile test. During the test, the width of the cracks was measured using an optical microscope. The results revealed that cracks caused a peak value in the strain curve of DFOS, which is dependent on the spacing of the cracks. The peak strains overlap when the cracking spacing is less than 20 mm, as there is a significant strain interference between the two adjacent strain peaks. Depending on the number and location of cracks, thirteen scenarios are classified and a corresponding procedure is proposed to evaluate the crack width by considering the strain redistribution of the cracked substrate. Validation tests demonstrated that the proposed procedure reduced the relative measurement error to 6.64%. Therefore, the developed procedure improves the accuracy of crack width evaluation based on DFOS in practical engineering applications.

Masonry arch bridges are an integral part of the European transportation infrastructure. Regular inspections are critical to ensure the safe operation of these bridges and also to preserve historical heritage. Despite recent advancements in assessment techniques, monitoring masonry arch bridges remains a difficult and important research topic. This paper describes a proof-of-concept study carried out on a masonry arch rail bridge in Gavirate, Italy, to investigate the dynamic responses of the bridge to train-induced moving loads. The dynamic measurements are obtained by a distributed fiber optic sensing system that enables a novel inspection of the integrity of masonry arch bridges. The focus of this field study is to quantify the dynamic strain induced by train moving loads and reveal the masonry arch bridge’s dynamic behaviors through the use of an innovative distributed fiber optical sensing-based technique. The results may provide a useful guideline for the application of distributed fiber optical sensing to monitoring masonry arch bridges.

Modal properties of dynamically tested wind turbine blades (WTBs) of a utility-scale wind turbine are identified. A comprehensive experimental program including free vibration and short- and long-term forced vibrations representing resonance and simplified fatigue conditions was carried out to investigate vibration-based features for damage diagnosis and prognosis. A set of 12 undamaged WTBs were tested to study the variability of the identified modal parameters. Results indicate that the variability of the natural frequencies was rather low, while the obtained damping ratios exhibited significant differences. Forced vibration tests were then conducted. To reach the failure of the blades, approximately 1.9 × 10⁴ and 4.2 × 10⁷ cycles were induced in the short- and long-term tests, respectively. Modal properties identified during testing protocols suggest that natural frequencies correlate well with damage. A linear finite element model was also developed, and its modal properties are compared to the identified modal parameters of the undamaged blades.

Large-span spatial structure damage identification is a challenging element of structural health monitoring. Compared with other buildings such as bridges and frames, space structures are characterized by large spans, many degrees of freedom and complex structures. Therefore, this paper proposes a new step-by-step damage identification method for spatial structures based on vibration signals. The method uses recurrence plot to process the structural vibration response to obtain nonlinear features. Through the nonlinear features reacting to different damage conditions of the structure and introducing convolutional neural network to realize the classification recognition problem under different damages. The feasibility analysis of step-by-step identification of damaged nodes and damaged rods is carried out with an orthogonal orthotropic quadrangular cone mesh structure model as an example. The optimized model training methods of data augmentation and migration learning are also introduced. An overall recognition accuracy of more than 89.7% is obtained. In order to realize the application of the proposed loss identification method in practical engineering, an operable GUI interface is constructed by encapsulating with programming technology. Afterwards, the complete step-by-step damage identification method from substructure to rod was verified by combining field tests and numerical simulations using a single-layer column surface mesh shell model consisting of 157 nodes and 414 rods. The results show that the damage recognition method has more than 85% recognition accuracy for structural damage. To explain the effectiveness of the convolutional neural network model training visualization of the recognition image features is performed using class activation heat maps.

The time-lag effect between temperature and thermal displacement may induce the displacement-based safety assessment results of long-span bridges to derivate from the truth. In this paper, the typical characteristics of the time-lag effect between temperature and thermal displacement are firstly investigated by using the synchronously monitored temperature and displacement data from a long-span steel box-girder arch bridge. And then, the inherent reasons of the time-lag effect are found out by employing the Kendall correlation coefficient. Following that, a general method derived from the Bayesian function registration model and the Z-mixture preconditioned Crank-Nicolson algorithm is proposed to compensate the time-lag effect. Finally, the proposed compensation method is verified by data from three bridges and compared with the traditional method achieved through shifting a fixed time interval. The results show that thermal displacement may be ahead of or lag behind temperature, depending on the temperature and thermal displacement of concern. The lag time varies from a few minutes to several hours with temperature and displacement variables, as well as time instants. The time-lag effect between temperature and thermal displacement is caused by the asynchronous change of the dominant temperature for the specific thermal displacement and other temperatures because of different material thermodynamic parameters and geometric characteristics of different bridge components. The developed compensation method can completely eliminate the time-lag effect between temperature and thermal displacement of various long-span bridges without any pre-correlation analysis and prior knowledge. The correlation between temperature and thermal displacement compensated by the method proposed in this paper is much stronger than that compensated by the traditional method.

This study presents a measuring framework for railway transition zones using a case study on the Swedish line between Boden and Murjek. The final goal is to better understand the vertical dynamics of transition zones using hammer tests, falling weight measurements, and axle box acceleration (ABA) measurements. Frequency response functions (FRFs) from hammer tests indicate two track resonances, for which the FRF magnitudes on the plain track are at least 30% lower than those at the abutment. The falling weight measurements indicate that the track on the bridge has a much higher deflection than the track on the embankment. Two features from ABA signals, the dominant spatial frequency and the scale average wavelet power, show variation along the transition zone. These variations indicate differences in track conditions per location. Finally, the ABA features in the range of 1.05–2.86 m⁻¹ are found to be related to the track resonance in the range of 30–60 Hz. The findings in this paper provide additional support for physically interpreting train-borne measurements for monitoring transition zones.

Bayesian finite element model updating has become an important tool for structural health monitoring. However, it takes a large amount of computational cost to update the finite element model using the Bayesian inference methods. The surrogate modeling techniques have received much attention in recent years due to their ability to speed up the computation of Bayesian inference. This study introduces two new surrogate models for Bayesian inference. Specifically, the radial basis function neural networks and fully-connected neural networks are used to construct surrogate models for the intractable likelihood function, avoiding the enormous computational cost of repeatedly calling the finite element model in the Monte Carlo sampling process. A full-scale numerical simulation of a concrete frame and a six-story steel frame experiment were selected as case studies. The trained surrogate models were used for Bayesian model updating, and the updated results were compared with the results obtained directly using the finite element model evaluation. The posterior distributions of the finite element model parameters obtained using the trained surrogate models are sufficiently accurate compared to those obtained using direct finite element evaluation. In addition, using surrogate models for finite element model updating greatly reduces computational costs.

Energy in Brazil is generated predominantly by hydroelectricity. The advantages of hydroelectric power include the fact that it is a clean source of energy. Nevertheless, the impoundment of great amounts of water represents a risk to the area downstream in the face of an accident. Even though the chances of rupture of a dam are small, the consequences are often catastrophic. Therefore, monitoring the structures of a dam is essential to prevent disasters to the environment and population and ensure the safety of its operation. This paper describes the implementation of a real-time online monitoring system for the dam of Campos Novos Power Plant. The concrete-faced rockfill dam (CFRD) is 202 m high and 592 m long on the crest. The system comprises a digital twin, a robotic total station (RTS) system, and other automated sensors. The digital twin is a tridimensional structural model of the dam. The finite element method is used to calculate displacements and stress state of the rockfill and concrete slab. RTS measurements are made hourly ensuring the safe operation of the dam.

Model Updating (MU) aims to estimate the unknown properties of a physical system of interest from experimental observations. In Finite Element (FE) models, these unknowns are the elements’ parameters. Typically, besides model calibration purposes, MU and FEMU procedures are employed for the Non-Destructive Evaluation (NDE) and damage assessment of structures. In this framework, damage can be located and quantified by updating the parameters related to stiffness. However, these procedures require the minimisation of a cost function, defined according to the difference between the model and the experimental data. Sophisticated FE models can generate expensive and non-convex cost functions, which minimization is a non-trivial task. To deal with this challenging optimization problem, this work makes use of a Bayesian sampling optimisation technique. This approach consists of generating a statistical surrogate model of the underlying cost function (in this case, a Gaussian Process is used) and applying an acquisition function that drives the intelligent selection of the next sampling point, considering both exploitation and exploration needs. This results in a very efficient yet very powerful optimization technique, necessitating of minimal sampling volume. The performance of this proposed scheme is then compared to three well-established global optimisation algorithms. This investigation is performed on numerical and experimental case studies based on the famous Mirandola bell tower.