Prediction Of Structural Damage Research Articles

Structural damage prediction based on Bayesian updating with Monte Carlo sampling

A series of engineering accidents in recent years have caused people to worry about the safety of engineering structures. As buildings continue to age, engineering accidents are increasing, and it is necessary to monitor their health. Bayesian updating can determine the probability of damage to a structure by collecting a posteriori information, and can more accurately estimate the location and extent of damage, thereby predicting the risk of structural damage and repairing the damage in time, significantly improving engineering efficiency. This paper attempts to update the information of the simulated structure based on the Bayesian updating method to obtain a more accurate posterior distribution of the potentially damaged components to demonstrate the feasibility and superiority of the technique. This paper finds that Bayesian updating can be used in conjunction with modeling software such as SMsolver. By constructing a structural model with SMsolver and processing the data with Bayesian updating, it is possible to calculate the damage problems of the structure accurately. At the same time, this paper also uses Monte Carlo sampling technology in combination with the Bayesian updating method to explore the prediction effect of structural damage. Using Monte Carlo sampling methods, the failure probability and damage location of the structure can be effectively estimated under the condition of known prior information, thereby improving production efficiency and the accuracy of structural inspection.

Enhancing pavement maintenance: A deep learning model for accurate prediction and early detection of pavement structural damage

Pavement distress, which is exacerbated by structural damage, results in massive carbon-intensity for routine maintenance and transportation safety issues. One promising approach to resolving this issue is to predict structural damage before it occurs. However, structural damage prediction is a complex problem with many influential factors that no single index can precisely solve for in-service pavement. In this study, a new method is adopted that combines all implicit factors into a deep learning neural network (DNN) algorithm to achieve high accuracy for the prediction of pavement structural damage. The training and optimization procedures of the DNN model are introduced explicitly for model repeatability and development. The results show that the state-of-the-art (SOTA) model performed well, with an accuracy rate of 88.46%. The contribution of this research facilitates the development of pavement maintenance decision systems to provide a prediction method for on-site pavement structures. By employing this approach, structural damage can be identified early to prevent further expansion, which will reduce costs and save lives and natural resources.

Fatigue health monitoring of AISI 304 notched specimens by means of thermographic analysis and UT-based measurements

The damage accumulation and the cracks development due to fatigue loads are the main failure mechanisms that occur in metal structures. Therefore, the prediction of structural damage is critically important for the safe and reliable operation of complex engineered systems. In the present work, the evolution of fatigue damage was monitored by infrared thermographic and ultrasonic measurements by means of piezoelectric sensors (PZTs) applied to a batch of notched specimens in AISI 304 stainless steel. Concerning the ultrasonic data, a linear and non-linear frequency study was carried out on the data acquired at various steps during the fatigue life through an appropriate MATLAB analysis algorithm based on the Fast Fourier Transform (FFT). In details, the proposed damage parameters related to thermal variations and the peak-to-peak signal voltage (Vpp) were considered to evaluate the fatigue damage process. Regarding the IRT technique, the thermographic monitoring inspection is implemented with measurements processed in MATLAB to estimate damage indices related to the fatigue damage of the same specimens. The results of the thermal analyses were finally compared with the data of the ultrasound measurements obtained to provide complementary information on the prediction of fatigue damage. The experimental results indicate an interesting increase in the proposed damage parameters that could be associated with an irreversible change due to fatigue damage at the notch tip. The final propagation phase of the crack, instead, is characterized by a quick data variation in agreement with the stiffness reduction. The applied Monitoring strategies has proved efficient for detecting damage induced by fatigue in metal materials.

Monitoring of stress distribution in damaged small-scale masonry walls by using two innovative sensors

Structural Health Monitoring (SHM) represents a strategic solution for the preservation of cultural heritage buildings. Existing masonry structures often suffer reductions in mechanical performances due to physiological aging of material constituents, external actions, and effect of catastrophic natural events. In many cases, the prompt prediction of damage in masonry elements is difficult and it can cause sudden collapses, compromising the safety of people.The proposed experimental study examines the effectiveness of two low-cost and innovative stress sensors, i.e. piezoelectric and capacitive stress sensors, for SHM of masonry structures. To this scope, the sensors were embedded in the mortar joints of two small-scale clay brick and calcarenite masonry wall specimens consisting of three panels. Experimental tests were carried out by applying a constant vertical compressive load at the top of each specimen and simulating the damage with a progressive reduction of the cross-section of one of the panels. During the tests, the vertical stress distributions (and their variations), were monitored by the sensors. Experimental outcomes from sensor reading were then compared to that numerically provided by a refined finite element simulation of the test. Results will show that vertical stress variations in masonry structures can be effectively accounted by the adopted sensors and potentially interpreted for the early prediction of structural damage.

Exploration Study on Energy Dissipation in Beam and Column Joint with Semi Rigid Connection

Abstract: The earthquake reconnaissance reports reveal that the failure of beam column joint has contributed to partial or complete collapse of reinforced buildings. The failure of a beam causes only localized effect whereas the failure of a beamcolumn joint causes instability of the whole building under seismic loading. This results in significant economic impact and loss of life. Hence it is essential to avoid failure of beam-column joint. The serious damage to the major structures during recent earthquakes is mainly attributed to lack of ductility in beam-column joint. In the present work, experimental investigation is carried out to study the effect of semi rigid connection in RCC beam column joints of reinforced concrete multi storey buildings. The effectiveness of semi rigid connection and effective positioning (stability approach) is analytically investigated for a typical four-bay four-storey and five-bay seven-storey RC frame with different percentage of joint connections by nonlinear static (pushover) and nonlinear dynamic (time-history) analyses. Poor performance was noted in rigid frame during the prediction of structural damage, lateralstrength and storey drift. The semi rigid connection isprovided in different places and it is found that performance of the structure enhanced in the stability approach. The stability approach exhibited improved performance in terms of roof displacement, for and semi-rigid connection.

A Brief Method for Rapid Seismic Damage Prediction of Buildings Based on Structural Strength

Rapid prediction of the post-earthquake structural damage to a region is of great importance to community relief and rescue. Detailed information on buildings in earthquake disaster areas is commonly inaccessible in the aftermath of an earthquake. Accurately assessing the seismic damage to urban buildings using limited information is significant. This study proposes a design-strength-based method for regional seismic structural damage prediction based on structural strength. Only a few basic attributes of buildings are required, including the basic building plan size, building height, construction time, and structural type. Theoretically, the method is very brief, and can be applied to all types of structures, including irregular ones, compared with other commonly used regional seismic damage prediction methods. The proposed method is validated with acceptable accuracy and efficiency compared with the refined finite element (FE) model analysis and simplified model analysis. The proposed seismic structural damage prediction method was applied to a university campus, which can serve as a simple reference for community earthquake resistance evaluation and improvement.

Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms

Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well-known ground motion intensity measures, are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial ground motion sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology is deployed in a user-friendly web application.

Numerical prediction of structural damage due to pressure buildup in subsea oil and gas equipment

Pressure buildup/annular pressure buildup in subsea oil and gas equipment occurs primarily due to the thermal expansion of trapped liquids. With the advent of modern computers, it has become increasingly possible to numerically analyze such problems with commercial codes available in the market. The objective of the present study is to propose a methodology for numerical prediction of structural damage in subsea oil and gas equipment due to pressure buildup. A judicious combination of computational fluid dynamics (CFD) with structural finite element analysis code has been used to perform a sample numerical analysis that is truly representative of a wide class of problems encountered in subsea oil and gas applications. The mitigation of trapped pressure is one among the prime areas of concern in the subsea oil and gas industry. In the present study, CFD analysis is used to determine the maximum pressure buildup due to the thermal expansion of trapped liquids in small leak tight enclosed volumes with rigid walls and the pressure obtained is used as a boundary condition for the structural analysis. In a nutshell, the analysis has been split into three steps (1) a steady-state CFD analysis to determine the temperature distribution within the oil and gas equipment under consideration, (2) the temperature contours obtained from the steady-state analysis are imposed as a boundary condition for the transient analysis to calculate the trapped pressure in the small volumes of interest and finally and (3) a structural analysis is used to determine the damage to the oil and gas equipment. The methodology adapted is similar to a one-way coupled fluid structure interaction analysis, but provides the added advantage of a significant reduction in computational cost. In the present study, the proposed methodology has been extended to a subsea Christmas tree (XT) and the pressure buildup in the hydraulic lines has been calculated. The results obtained using the present technique has been compared with analytical solution. The proposed numerical technique can be applied to any subsea or surface oil and gas equipment where pressure buildup due to trapped volume is a major issue. The findings of this study can help for better understanding of pressure buildup in trapped volumes within subsea/surface oil and gas equipment. This study can be applied to predict the thermal expansion of trapped volumes in subsea XTs, manifolds, pipe line end manifolds (PLEM) and pipe line end termination (PLET) units.

Two-Level Quality Decision Support System for Building Structural Damage Prediction and Maintenance Solution Recommendation in the Operation and Maintenance Phase

AbstractTraditional building quality management in the operation and maintenance (O&M) phase of the building life cycle has concentrated on inspection of quality defects by using new equipment or m...

The Architecture Design and Implementation of Aircraft Structural Fault Assistant Decision System Based on Data Analysis

That the aircraft structural damage diagnosis and prediction is very important for flight safety. In this paper, the aircraft structural damage can be predicted based on the maintenance data analysis and the structure fault diagnosis and assistant decision are realized. Thus, the maintenance management of aircraft structure damage can be developed. According to the actual service data, both the structural damage prediction and decision assistant support system are designed, which apply the micro-service architecture and Vue.Js front-end development technology. Thus, the effective container server and excellent usability are achieved.

Prediction Method of Three‐Dimensional Crack Propagation Path Based on Deep Learning Application

In engineering, most structural components suffer from local damage in service or fabrication. Under different loading conditions, such local damage will further expand to large cracks and structural collapse. For ensuring longevity and reliability, the study of brittle crack growth is important. In structural damage prediction, various methodologies are commonly used and have demonstrated success in the past. Most traditional simulation methods, however, have the problems of large calculation quantities and long calculation times. At the same time, the mutual influence of crack opening stress and crack image on crack widening is rarely considered in existing works based on the deep learning framework. Herein, a deep learning model is developed to predict the spread of fracture of brittle materials under stress to improve the aforementioned problems. By developing a nonlinear relationship between crack stress and damage effects, this approach predicts crack growth, thus avoiding the loss of required information on crack growth behavior. The model emphasizes repeatability and extensibility, which is verified using extended finite element method (XFEM) Abaqus software through multiple sets of material crack data generated. This model's potential applications are material design and evaluation of residual life.

Dent damage identification in stiffened cylindrical structures using inverse Finite Element Method

The offshore industry has been using stiffened thin-walled steel cylindrical structures for decades, particularly as the columns of floating offshore installations. The floating offshore installations may be subjected to severe marine environmental conditions. Accidents such as collisions may also occur. Structural Health Monitoring (SHM) is a viable tool to maintain safe operation of offshore installations. Inverse Finite Element Method (iFEM) is one of the most powerful methods for SHM process. Hence, this study focuses on the application of iFEM methodology to thin-walled cylindrical structures representing the columns of floating offshore installations. iFEM methodology is verified by comparing its displacement results against reference finite element method (FEM) solution. After this verification, four different damage cases with different size, location and number of damages are considered. By using a newly introduced damage parameter and von Mises strain distribution iFEM accurately identified the correct damage locations and sizes. Therefore, it is concluded that iFEM can be used for structural damage prediction in offshore structures with high accuracy even if the number of the strain sensors is limited.

Measuring rutting dimension and lateral position using 3D line scanning laser and inertial measuring unit

Pavement rutting caused by repeated traffic loads is a common type of structural damage to pavement. Rutting dimensions and lateral position are strongly related to traffic safety and ride comfort. However, in the field, rutting depth is usually measured by traditional methods, and rutting lateral position is ignored due to the limitations of data sources and algorithms. This study provides an automatic method for rutting dimension and lateral position measurement using a 3D line scanning laser system and Inertial Measuring Unit (IMU). Rutting dimensions (e.g., width, depth, depression area, wall slope and spacing between the inner and outer rutting) and locations (e.g., lateral position and rutting deviation away from lane center line) are measured by fusing multivariate data (e.g., 2D pavement images, transverse profiles and IMU data). In this study, two straight road sections located in Fuzhou, China, are chosen to validate the reliability and robustness of the proposed algorithm. Findings indicate that the measurement results from the new algorithm are highly correlated with the ground truths manually collected in the field. This study would benefit transportation agencies in traffic safety analysis, ride comfort evaluation and structural damage prediction based on the continuous pavement rutting dimension and its lateral positions.

Research on the damage prediction method of offshore wind turbine tower structure based on improved neural network

Tower structure is a basic part of offshore wind power system, and how to ensure the stability and safety of tower structure in wind turbine operation and develop the efficient and accurate damage prediction method to judge the damage location and degree of tower structure has become an important research topic in the offshore wind power industry. With high adaptability, nonlinearity and strong function approximation ability, Artificial neural network (ANN) has been widely used in structural damage prediction and showed better performance than other diagnostic methods, however, traditional Back Propagation neural network (BPNN) has shortcomings of complex training parameters and low accuracy. To address this issue, this paper considered an improved neural network damage prediction method based on step-by-step identification for offshore wind turbine tower structure, an experimental research was carried out to simplify wind turbine model and experimentally measure the intact steel cylinders with different damage locations and different damage degrees. Then, the modal analysis was carried out by using the numerical analysis method and the results were compared with those of experimental measurement to modify the model. The training data are generated by Abaqus software to verify the proposed method. Results showed that the step-by-step prediction method could effectively reduce the complexity of the network, improve the prediction accuracy of the neural network, and save the training time.

Online Nonlinear Structural Damage Detection Using Hilbert Huang Transform and Artificial Neural Networks

Structural Health Monitoring (SHM) as a process in order to implement a damage detection strategy and assess the condition of structure plays a key role in structural reliability. In this paper, we aim to present a methodology for online detection of damages which may occur during a strong ground excitation. In this regard, Empirical Mode Decomposition (EMD) is superseded by Ensemble Empirical Mode Decomposition (EEMD) in the Hilbert Huang Transformation (HHT). Albeit analogous, EEMD brings about more appropriate Intrinsic Mode Functions (IMFs) than EMD. IMFs are employed to assess the first mode frequency and mode shape. Afterward, Artificial Neural Network (ANN) is applied to predict story acceleration based on previously measured values. Because ANN functions precisely, any congruency between predicted and measured acceleration indicates onset of damage. Then, another ANN method is applied to estimate the stiffness matrix. Though the first mode shape and frequency are calculated in advance, the process essentially requires an inverse problem to be solved in order to find stiffness matrix, which is done by ANN. This algorithm is implemented on moment-resisting steel frames, and the results show that the proposed methodology is reliable for online prediction of structural damage.

Real-time monitoring of damage evolution by nonlinear ultrasonic technique

In this work, the ultrasound technique was used to monitor the damage of material subjected to fatigue loads. Prediction of structural damage is critical for safe and reliable operation of engineered complex systems. In these measurements, conventional ultrasonic probes (transmitter and receiver) were stably fixed to the tested samples with steel brackets, in order to eliminate ever possible variability associated with the coupling of probes. The transmitted and received ultrasonic signals were recorded and analyzed using a digital oscilloscope. The data were converted into the frequency domain using an algorithm developed in Matlab based on Fast Fourier Transform (FFT) for received signal in dependence of the applied stress level and the accumulated fatigue damage was deeply studied in order to recognize quantitative effects, suitable for an experimental prediction of the integrity of the material. The acquired data were compared with the reference signal, at the beginning of the fatigue tests. Particular care has been paid to UT signal attenuation and to the study of the frequency spectrum as the number of load cycles varies. The applied experimental technique has proved efficient for detecting damage induced by mechanical stress.

A response spectrum-based indicator for structural damage prediction

Improving predictive relationships between strong ground motion and seismically induced damage in buildings is an important topic for seismic risk assessment. Spectral acceleration at a structure’s fundamental period Sa(T1) has often been used as the indicator of the potential damage a given ground motion may induce on structural systems. However, such a scalar indicator (Damage Potential Indicator, DPI) captures only limited information about the ground motion time history and response spectrum shape. In a severely damaged state, the effective period of a structure will change, which makes the structure respond to other parts of the response spectrum. In this study, a new response spectrum-based DPI that contains an intensity component and a spectral shape component is proposed. The critical period range of the response spectrum that should be considered in spectral shape evaluation is determined using a circle rule for bilinear single-degree-of-freedom (SDOF) systems. The effectiveness of the newly proposed DPI is validated by comparing damage potential similarity using a database of ground motion records.

Modeling of the Onset, Propagation, and Interaction of Multiple Cracks Generated From Corrosion Pits by Using Peridynamics

High stress regions around corrosion pits can lead to crack nucleation and propagation. In fact, in many engineering applications, corrosion pits act as precursor to cracking, but prediction of structural damage has been hindered by lack of understanding of the process by which a crack develops from a pit and limitations in visualization and measurement techniques. An experimental approach able to accurately quantify the stress and strain field around corrosion pits is still lacking. In this regard, numerical modeling can be helpful. Several numerical models, usually based on finite element method (FEM), are available for predicting the evolution of long cracks. However, the methodology for dealing with the nucleation of damage is less well developed, and, often, numerical instabilities arise during the simulation of crack propagation. Moreover, the popular assumption that the crack has the same depth as the pit at the point of transition and by implication initiates at the pit base has no intrinsic foundation. A numerical approach is required to model nucleation and propagation of cracks without being affected by any numerical instability and without assuming crack initiation from the base of the pit. This is achieved in the present study, where peridynamics (PD) theory is used in order to overcome the major shortcomings of the currently available numerical approaches. Pit-to-crack transition phenomenon is modeled, and nonconventional and more effective numerical frameworks that can be helpful in failure analysis and in the design of new fracture-resistant and corrosion-resistant materials are presented.

THU0155 Sensitivity to Change of the Ultrasound Disease Activity Score

BackgroundWe have investigated construct validity and reliability of the Ultrasound Disease Activity Score (USDAS) compared with DAS28 in assessing joint inflammation and in prediction of structural damage in patients with...

English

Introduction Ankylosing spondylitis (AS) is a chronic inflammatory rheumatic disorder with unclear pathogenesis. Bone formation is a hallmark feature of AS, precise mechanisms of which are unknown. The aim of this review was to summarize the current knowledge regarding both clinical significance and pathogenesis of bone formation in AS. Materials and methods Articles, published in the medical literature and chapters in textbooks related to the discussed topic, were critically reviewed and relevant data were selected, collated and organized for the purpose of this manuscript. Results Conventional radiography is the most valuable classical tool for identification of structural changes in AS. The typical manifestations of bone formation in AS usually involve the axial skeleton, including spine, sacroiliac and hip joints, and, if properly recognized, have an important role in diagnosing, classifying and monitoring patients. Molecular pathways leading to bone formation in AS have not been elucidated sufficiently. Abnormally active signalling by wingless-type (wnt)-like and bone morphogenic protein (BMP)-mediated pathways have been suggested as major contributors to diseaserelated ossification in AS. The only medicines, shown currently to delay the process of bone formation in AS patients, are nonsteroidal anti-inflammatory drugs. Conclusion Better understanding and prediction of structural damage would help to improve and individualize management of patients suffering from AS.