In-service Structures Research Articles

Comparison of destructive and non-destructive fracture toughness measurements for Q235 steel butt-welded joint

As a critical component of a welded structure, the integrity of the welded joint significantly affects the safety and reliability of the structure in service. Therefore, it is essential to investigate the integrity of welded joints. However, given the harshness of standard measurements renders them unsuitable for application to small volume zones, welded structures or in-service structures. In this study, the fracture properties of a Q235 steel butt-welded joint was evaluated by boundary effect modelling (BEM) method and the ball indentation (BI) method. The results demonstrate that the fracture toughness distribution obtained by the two methods are consistent, with the highest fracture toughness in the weld metal, followed by the heat-affected zone and the base metal, and the lowest fracture toughness in the fusion zone, which is in agreement with the results of the metallographic testing. Furthermore, the relative errors of the zones obtained by the ball indentation method and the boundary effect modelling method ranging from 10.16 to 19.05%, which indicates safety margin ought to be considered for employing the BI method to determine fracture properties of in-service structures.

Reactive molecular dynamics of the fracture behavior in geopolymer: Crack angle effect

Reactive molecular dynamics was applied in this study to construct the sodium aluminosilicate hydrate (N-A-S-H) and tensile fracture models with various crack angles. The impact of crack angle on the behavior of N-A-S-H fractures was explored while considering structural mechanical properties and energy evolution. Furthermore, the fracture toughness and brittleness index for various crack angle models were calculated. The findings indicated that the ultimate strength and elastic modulus of the fracture models grew linearly with the increase in crack angle. The fracture toughness value progressively grew while the model’s elastic energy efficiency and new surface energy efficiency decreased simultaneously. The 45° crack model possessed the largest oblique crack development surface in the fracture process due to the coupling effect of tensile and shear stress. Its elastic energy efficiency decreased as well the most, while the new surface energy efficiency increased and the fracture toughness value dropped sharply. It is crucial to place a stronger emphasis on spotting cracks both in the in-service structures or structures being demolished. This ensures optimal performance and safety by enabling more effective adjustments to the direction of external forces and energy input.

Dynamic force identification considering modeling errors using modal expansion method and relevant vector regression algorithm

Accurate identification and estimation of external forces on launch vehicles, aircraft, and other in-service engineering structures plays a vital role in structural design and health monitoring. Considering structural modeling errors, this paper develops a novel force identification method based on modal expansion and relevant vector regression (RVR). Initially, the incomplete and noisy experimental modal data are used to modify the stiffness and mass matrices of the finite element (FE) model, reducing the impact of modeling errors on force identification accuracy. Subsequently, to account for noise disturbances and potential information about unknown initial conditions in the measured acceleration responses, the external forces and the unknown initial conditions are respectively expanded using trigonometric functions and the mode shapes based on the function fitting technique. On this basis, the force identification equation is established for the updated model. The RVR algorithm is integrated into modal expansion and force identification. Firstly, the mode shapes of the FE model are utilized to expand the incomplete and noisy experimental mode shapes to their full degree of freedom, significantly enhancing the accuracy of the model updating. Secondly, by combining the measured acceleration responses, the base coefficients can be solved sparsely, effectively identifying the dynamic forces even when unknown initial conditions are present.

Deformation stage identification in steel material using acoustic emission with a hybrid denoising method and artificial neural network

Acoustic emission (AE) is widely used for identifying source mechanisms and the deformation stage of steel material. The effectiveness of this non-destructive monitoring technique heavily depends on the quality of the measured AE signals. However, the AE signals from deformation are easily contaminated by the signals from noise in a noisy environment. This paper presents a hybrid model for deformation stage identification, which combines a self-adaptive denoising technique and an Artificial neural network (ANN). In pursuit of model generality, AE signals were collected from tensile coupon tests with various steel materials and loading speeds. First, a decomposition-based denoising method is applied based on the singular spectral analysis (SSA) and variational mode decomposition (VMD), which is defined as SSA-VMD. Its effectiveness is demonstrated by simulated signals and experimental results. Following the use of the denoising technique, an ANN is constructed to identify the deformation stage of steel materials with the input of features extracted from the filtered AE signals. The results indicate that the ANN achieves a high prediction accuracy of 0.93 in the test set and 0.87 in unseen data. By applying this denoising method, the ANN-based approach enables accurate correlation of the collected AE signals to deformation stages. The finding can be used as the basis for the creation of new methodologies for monitoring structural health status of in-service steel structures.

Remaining Useful Life for Heavy-Duty Railway Cast Steel Knuckles Based on Crack Growth Behavior with Hypothetical Distributions

The current research on the integrity of critical structures of rail vehicles mainly focuses on the design stage, which needs an effective method for assessing the service state. This paper proposes a framework for predicting the remaining useful life (RUL) of in-service structures with and without visible cracks. The hypothetical distribution and delay time models were used to apply the equivalent crack growth life data of heavy-duty railway cast steel knuckles, which revealed the evolution characteristics of the crack length and life scores of the knuckle under different fracture failure modes. The results indicate that the method effectively predicts the RUL of service knuckles in different failure modes based on the cumulative failure probability curves for different locations and surface crack lengths. This study proposes an RUL prediction framework that supports the dynamic overhaul and state maintenance of knuckle fatigue cracks.

Stochastic force identification for uncertain structures based on matrix equilibration and improved Tikhonov regularization method

Accurate identification and estimation of stochastic forces applied to in-service engineering structures play a vital role in structural safety assessments. This study devised an effective force power spectral density (PSD) identification method to address the challenge of identifying multipoint stationary stochastic forces in uncertain structures. Initially, a probability model was employed to characterize structural uncertainties. Subsequently, an integral relationship was established between the probability density function (PDF) of the random structural parameters and that of the stochastic force PSD. By employing a point-selection technique based on the generalized F-discrepancy and a smoothing method, the uncertainty problem was transformed into a finite number of stochastic force PSD identification problems for deterministic structures. Simultaneously, based on the inverse pseudo-excitation method, a matrix equilibration approach and an improved Tikhonov regularization method were used to address the problem of large identification errors near structural natural frequencies. In comparison to the traditional weighting matrix method, the proposed method further reduces the condition number of frequency response function matrices, thereby enhancing the accuracy of force PSD identification. Finally, numerical examples were presented to validate the effectiveness of the proposed method in solving the stochastic force identification problem.

Research on a Three-Way Decision-Making Approach, Based on Non-Additive Measurement and Prospect Theory, and Its Application in Aviation Equipment Risk Analysis.

Due to the information non-independence of attributes, combined with a complex and changeable environment, the analysis of risks faces great difficulties. In view of this problem, this paper proposes a new three-way decision-making (3WD) method, combined with prospect theory and a non-additive measure, to cope with multi-source and incomplete risk information systems. Prospect theory improves the loss function of the original 3WD model, and the combination of non-additive measurement and probability measurement provides a new perspective to understand the meaning of decision-making, which could measure the relative degree by considering expert knowledge and objective data. The theoretical basis and framework of this model are illustrated, and this model is applied to a real in-service aviation equipment structures risk evaluation problem involving multiple incomplete risk information sources. When the simulation analysis is carried out, the results show that the availability of this method is verified. This method can also evaluate and rank key risk factors in equipment structures, which provides a reliable basis for decisions in aviation safety management.

Experimental and numerical simulation studies of electrical resistance of cementitious materials under varying temperature history

Many in-service concrete structures are normally exposed to varying temperature condition. The time-dependent and non-uniform temperature distribution in concrete structures could affect the accurate assessment of their electrical properties. Therefore, the influences of temperature change over time and the spatial variation in temperature on the measured electrical resistance of cementitious materials are still in need of investigation. The coupled thermal-electrical responses of two types of cementitious materials under varying temperature history were investigated by a series of thermal and electrical tests in this study. Then a coupled thermal-electrical finite element (FE) model was developed to simulate the coupled thermal-electrical responses. The results show that the input direct current has a minor effect on the measured electrical resistance by embedded four-probe method and the specimen temperature when it is no greater than 3 mA. The relationship between the measured temperature at 10-mm depth of the specimen and the apparent resistance is well captured by a linear equation. The coupled thermal-electrical finite element model can predict well the developments of specimen temperature and apparent electrical resistance under varying temperature history. The FE results reveal that the non-uniform temperature variation results in the redistribution of electrical current within the tested specimen. However, electrical field between the two inner electrodes is always uniform when the embedded four-probe method is used, which is not affected by the non-uniform temperature variation.

Combined Passive and Active Ultrasonic Stress Wave Monitoring of Concrete Structures: An Overview of Data Analysis Techniques and Their Applications and Limitations

Combined passive [or acoustic emission (AE)] and active ultrasonic stress (US) wave monitoring has been shown to provide a more holistic picture of ongoing fracture processes, damage progression, as well as slowly occurring aging and degradation mechanisms in concrete structures. Traditionally, different data analysis techniques have been used to analyze the data generated from these two monitoring approaches. For AE data analysis, for instance, signal amplitudes, hit rates, source localization, and b-value analysis have been used to detect and locate cracking. On the other hand, amplitude tracking, magnitude squared coherence (MSC), and coda wave interferometry (CWI) are examples that have been employed for US data analysis. In this presentation, we explore these data analysis techniques and show where their respective applications and limitations might be. After providing an overview of the monitoring approach and the different data analysis techniques, results and observations from select laboratory experiments, as well as an in-service structure are discussed. Finally, suggestions for further work are proposed.

Damage and fracture in in-service concrete structures subjected to large diurnal temperature variations based on phase-field approach

In-service concrete structures are exposed to the natural environment for extended periods, making them susceptible to performance degradation and cracking due to large diurnal temperature ranges (DTR). This paper presents the development of a novel thermo-mechanical-fatigue coupling phase-field model to investigate the cracking process in in-service concrete structures under varying DTR conditions. The results indicate that concrete walls exposed to different single-day DTRs (20–60 ℃) exhibit phase-field damage without cracking. The phase-field damage is negligible at a 20 ℃ DTR but increases to 0.41 at a 60 ℃ DTR. Considering temperature fatigue, the concrete wall initiates cracking when the DTR exceeds 40 ℃ within 100 cycles. With an increase in DTR from 40 ℃ to 50 ℃ and 60 ℃, the time for crack nucleation decreases from 70 cycles to 29 and 25 cycles, respectively. Concrete walls subjected to 40 ℃ and 50 ℃ DTR exhibit a single crack on the top surface, whereas those exposed to a 60 ℃ DTR have two cracks on the top surface and one crack on each side. Furthermore, the cracks induced by the DTR are surface cracks that exhibit rapid expansion up to a specific length of approximately 140 mm before stabilizing.

Implantable sensing technology for civil engineering structures

Providing crack location and stiffness variation information using practical structural health monitoring technology is highly significant for in-service structures. Inspired by the fast development of implantable devices, this paper introduced an implantable sensing technology (IST) for health monitoring of a typical concrete member known as shear wall undergoing loading progression. Two collaborative monitoring steps were developed to acquire targeted damage information at different loading stages, the one was, a cylindrical concrete implantable module (CCIM) sensor-enabled IST was applied for monitoring and imaging the early cracks of the shear wall at the initial loading stage; and the other was, a concrete implantable bar (CIB) sensor-enabled IST was employed for monitoring and assessing the damage evolution at the mid-late loading stage. Experimental results showed that these two collaborative ISTs can accurately identify the early cracking regions of the shear wall and reveal its stiffness degradation processes throughout the loading.

Transfer Learning-Based Structural Damage Identification for Building Structures with Limited Measurement Data

Structural damage detection is crucial for ensuring the safety of civil building structures in operational environments. Recently, deep learning-based methods have gained increasing attention from engineers and researchers. The performance of conventional deep learning methods for structural damage detection relies on a large number of labeled training datasets. However, it is difficult or/and impossible to obtain sufficient datasets to cover various damage scenarios for in-service structures. A little research has been conducted to identify both the damage severity and location with limited labeled measurement data. A novel transfer learning-based method for structural damage identification with limited measurements has been proposed utilizing frequency response functions (FRFs) as the input. The real structure is regarded as the target domain and its numerical model is as the source domain. The samples for various damage scenarios are generated using the numerical model, and a designed deep convolutional neural network (CNN) is pre-trained. The knowledge of the pre-trained network is transferred to identify the damage location and severity of the real structure using limited measurement data. Numerical and experimental studies have been conducted on a three-story building structure to verify the performance of the proposed method. To understand transfer learning and model interpretability, the t-SNE feature visualization is adopted to show the feature distribution changes during transfer learning. Numerical and experimental results show that the proposed approach outperforms conventional CNN models, and it is effective and accurate in identifying structural damage location and severity in real structures with limited measurement data.

Upcrossing‐based time‐dependent resilience of aging structures

The time-dependent resilience of an in-service aging structure provides quantitative measure of the structural ability to prepare for, adapt to, withstand and recover from disruptive events. Resilience models have been proposed in the literature to evaluate the resilience of aging structures subjected to discrete load processes, which are, however, not applicable to handle resilience problems considering continuous load processes. In this paper, a new method is developed to evaluate the time-dependent resilience of aging structures subjected to a continuous load process. The proposed method serves as the complement of the existing resilience models addressing discrete load processes, and takes into account the aging effects of the structural resistance/capacity and the nonstationarity in loads as a result of climate change. A structure suffers from a damage state upon the occurrence of an upcrossing of the load effect with respect to the resistance/capacity, leading to the reduction of the performance function, followed by a recovery process that restores the performance. The proposed method enables the time-dependent resilience to be evaluated via a closed form solution. It is also revealed that, the proposed resilience model takes an extended form of the existing formula for upcrossing-based time-dependent reliability, thus establishing a unified framework for the two quantities. The applicability of the proposed method is demonstrated through examining the time-dependent resilience of a residential building subjected to wind load. The effects of key factors on resilience, including the nonstationarity and correlation structure of the load process, as well as the resistance/capacity deterioration scenario, are investigated through an example. In particular, the structural resilience would be overestimated if ignoring the potential impacts of climate change, which is a relatively non-conservative evaluation.

Using crack width for shear, stiffness, and stirrup strain history predictions for reinforced concrete beams

Shear failures in reinforced concrete structures occur with little or no warning. Reinforced concrete members with shear cracks should be evaluated to ensure safety. Existing evaluation methods have large variability, require time-consuming modeling or expert opinion. This study uses machine learning to investigate correlations of crack width with shear loading, stiffness, and stirrup strain histories. Experimental literature enables the assembly of a database of rectangular reinforced concrete slender beams with crack width measurements for beams with shear reinforcement amounts smaller and larger than the minimum required by ACI 318-19. Measured data include crack widths from 122 beams, load–displacement relationship from 100 beams, and stirrup strains from 46 beams. Gaussian Process Regression is used to correlate crack width and geometric, material and design properties to shear loading, stiffness, and stirrup strain histories. Ten-fold cross validation training shows mean absolute percent errors of 18, 33 and 77% for shear, stiffness, and stirrup strain history predictions. The proposed algorithms can be used to accelerate the evaluation of in-service structures and can be updated upon availability of additional data.

Prediction of residual performance of corroded RC beams from observed surface cracks by 3D RBSM with MPC

Corrosion of reinforcing steel in concrete structures can significantly compromise their performance. Accurately estimating this deterioration for in-service structures is challenging due to uncertainties surrounding the distribution of internal corrosion and cracking patterns, compounded by the limitations of destructive testing. This research proposes a novel approach to predict the remaining load-bearing capacity of corroded reinforced concrete (RC) structures based on the pattern of surface cracks caused by corrosion. The method integrates a model predictive control (MPC) algorithm with a three-dimensional Rigid Body Spring Model (3D RBSM) to estimate internal corrosion levels from observed cracks and a normal 3D RBSM simulation to predict the residual performance. The proposed system is validated through analysis of four corroded beam specimens based on experimental work reported in the literature. In use, the MPC algorithm first estimates a corrosion distribution in a RC beam and generates simultaneous corrosion-induced crack and stress predictions. This information is then used to predict residual load-bearing capacity. The system accurately forecasts the non-uniform corrosion distribution, flexural capacity, and load-displacement curves of a specimen, offering insight into the performance differences among the specimens and demonstrating its potential to assess the serviceability of existing RC structures with corrosion issues.

A Lorentz force-based SH-typed electromagnetic acoustic transducer using flexible circumferential printed circuit

Structural health monitoring (SHM) of in-service structures is becoming increasingly important. The fundamental shear horizontal (SH0) guided wave mode in plate-like structures shows great potential in damage detection due to its non-dispersive and in-plane vibration properties. In order to generate SH0 waves, a practical Lorentz force-based electromagnetic acoustic transducer (EMAT) was introduced in this study using the flexible circumferential printed circuit (CPC). The designed principle of CPC-EMAT was similar to that of the circumferential magnet array (CMA)-based EMAT. However, the structure of the CMA-EMAT is complex, and it is difficult to assemble for generating high frequency and uniformly distributed omnidirectional SH0 waves. Firstly, the performance of the CMA-EMAT with different numbers of magnets was investigated by finite element simulations. Then, the CPC was proposed to replace the CMA with an optimized designed on its size. The CPC-EMAT is easier to fabricate compared to the CMA-EMAT. Finally, experimental tests were conducted for systematic validations on the transducer properties. Simulation and experimental results show that the CPC-EMAT can successfully generate the desirable and acceptable omnidirectional SH0 waves. The proposed CPC-EMAT is anticipated to find widespread application in SH-typed guided wave-based SHM.

Dynamic probability modeling-based inspection intervals optimization for civil aircraft composite structures using Bayesian updating

The evaluation of damage tolerance in composite materials is essential for ensuring the safety of aircraft structures. One of the most challenging aspects of applying probability modeling-based methods to evaluate damage tolerance is determining the actual damage size distributions for in-service aircraft structures. Although existing nondeterministic approaches have been used to optimize inspection intervals of composite structures, few studies have investigated the effects of updates on the actual damage size distribution and its impact on both the probability of structural failure and inspection intervals. This paper proposes a dynamic optimization method for inspection intervals of composite structures based on Bayesian updating. The damage size distribution of the composite structure is characterized by a general stochastic distribution. A Bayesian updating methodology is presented to iteratively update the actual damage size distribution whenever new data becomes available. Based on the constructed probability model, the inspection intervals of composite structures are determined under the objectives of optimal safety and economy for civil aircraft using a Monte Carlo approach. Compared to prior distribution models, the proposed method achieves higher safety for structures during a single inspection, reduces the failure probability of structures throughout their entire service life, and incurs lower maintenance costs. It also enables maintenance personnel to flexibly adjust inspection intervals while facilitating quantitative evaluation of both failure probabilities and maintenance costs associated with these intervals. These findings suggest that the proposed method holds great potential in enabling maintenance personnel to make informed decisions regarding inspection intervals for improved safety and economic performance.

Real-time safety risk assessment of floating offshore platforms based on on-site monitoring

Real-time safety risk assessment of in-service marine engineering structures is one of the key components for the intelligent development of marine resources. Floating platforms typically have weak modes and time-varying characteristics, and hence, traditional structural damage identification and safety assessment methods cannot be directly applied to floating platforms. The objective of this study was to achieve the real-time safety risk assessment of in-service floating platforms. First, a structural damage identification method for floating platforms was proposed based on virtual loads, and the physical parameters of the platforms were obtained through inversion of measurable data. The identification result was verified based on the lengths and stiffnesses of polyester cables, which affected the dynamic response of the floating platform. The results showed that the proposed method effectively detected the parameter changes of the platform during long-term service. Then, the neural network was used to construct a load–stress surrogate model at key positions of the floating offshore platform. This model could calculate the stresses at key positions in real time, thereby evaluating the safety risks of the platform. Lastly, a real-time safety risk assessment method for floating offshore platforms was proposed, which provides a technical solution for ensuring the safety of offshore platforms.

Study of the kurtoses transmission of linear structures under stationary non-Gaussian random loadings

Recent research has discovered that the kurtoses of non-Gaussian stress loadings can significantly impact the fatigue life of in-service structures. To predict potential damage, a fatigue life estimation method based on Gaussian damage and a kurtosis-related corrective coefficient has been developed. However, the transmission of kurtoses from excitations to responses has not been well studied, particularly in multi-input cases. This hinders efforts to accelerate fatigue damage by controlling input kurtoses. Therefore, this paper aims to establish a kurtoses transmission model for a linear structure under multiple uncorrelated stationary non-Gaussian excitations generated by zero-memory non-linear method. Firstly, a single-input kurtosis transmission equation is proposed to establish the relationship between excitation kurtosis and response kurtosis. Using this equation, the response kurtosis is formulated with input kurtosis and the system parameters of a linear structure. Secondly, the response kurtosis of multi-input cases is deduced, and the study shows that the response kurtosis is the weighted sum of the kurtosis induced by each excitation. Finally, numerical validation and experimental studies are conducted to verify the accuracy of both the single-input and multi-input kurtoses transmission models. The validations demonstrate that the proposed models can predict the response kurtoses with satisfactory precision, regardless of the input spectral shape and the number of uncorrelated input forces.

Mechanical performance of Q345 steel after corrosion: Experimental and theoretical investigation

Corrosion significantly influences the mechanical performance of steel, and the accurate prediction of stress-strain curves (S-SC) for steel after corrosion is crucial for the safety evaluation of in-service steel structures in engineering. This study focuses on assessing the degradation performance of Q345 steel following corrosion. Copper accelerated acetic acid salt spray testing (CASS) is employed to corrode Q345 specimens, and a power model is established to relate the mass loss rate to the corrosion time of Q345 steel. Uniaxial tension tests are then conducted to observe the mechanical behavior of corroded Q345 steel. Results indicate that the strength and ductility of Q345 steel decrease with an increase in the mass loss rate, and the yield plateau of the S-SC gradually shortens until it disappears. With the increase of corrosion time, the microstructure of the steel surface is transformed from spherical structure to needle-like structure. Two constitutive models are developed, incorporating the S-SC with and without the yield plateau, based on existing models. These models demonstrate good agreement with various test data, providing valuable insights into the mechanical behavior of corroded Q345 steel.