Structural Reliability Research Articles

Settlement of a Pile Foundation Considering Linear and Rheological Properties of Soils

Despite numerous studies of single piles and practical experience with their application, methods for calculating settlements of pile foundations remain limited. The existing objective need for specialized methods of pile foundation settlement calculation that take into account the rheological properties of the base soils is becoming more and more important, especially in the construction of unique objects in complex ground conditions. When predicting the stress–strain state of the pile–raft-surrounding soil mass system, it is allowed to consider not the entire pile foundation as a whole, but only a part of it—the computational cell. In the present work, we have solved the problems of determining the strains of the computational cell consisting of the pile, the raft and the surrounding soil according to the column pile scheme and hanging pile scheme, on the basis of the Kelvin–Voigt rheological model, which is a model of a viscoelastic body consisting of parallel connected elements: Hooke’s elastic spring and Newtonian fluid. According to our results, we obtained graphs of the dependence of strains of the computational cell on time at different pile spacing and different values of coefficients of viscosity of the surrounding soil, and a formula for calculating the reduced modulus of deformation of the pile. The results of the present study can significantly improve the accuracy of calculations during construction on clayey soils with pronounced rheological properties and, as a result, increase the reliability of pile structures in general.

Evidence of Internal Structure Validity, Measurement Invariance, Convergent Validity, Clinical Validity, and Reliability of the Woman Abuse Screening Tool in Peruvian Women.

Intimate partner violence (IPV) is the most common type of violence committed against women and results in serious personal, familial, social, and economic consequences; thus, there is a need to detect IPV early. One test for detecting IPV is the Woman Abuse Screening Tool (WAST). The objective of the study was to obtain evidence of the validity of the WAST in terms of its internal structure, measurement invariance, convergent validity, clinical validity, and reliability. A total of 670 women who have or have had a heterosexual relationship participated in the study. The first stage included 513 adult women who partook of health services (M age = 35.5 years, SD age = 10 years). The second stage included 160 women, 80 diagnosed with IPV (M age = 34.2 years, SD age = 11.8 years) and 80 who were not (M age = 31.8 years, SD age = 11.5 years). We used multivariate and receiver operating characteristic curve analyses. We found evidence that the WAST demonstrates a one-dimensional structure; evidence of measurement invariance regarding cohabitation with the partner, length of the relationship, and age; evidence of convergent validity in terms of correlations with emotional dependence, self-compassion, expressive suppression, anxiety, and depression; and evidence of clinical validity in terms of the high probability of detecting positive cases of IPV. These properties support the use of the WAST for detecting possible cases of IPV, which will allow timely intervention. This instrument can also be used in larger studies on IPV in the Spanish-speaking population.

Forecasting bridge damage within a predictive Structural Reliability-based DSS

Nowadays there is urgent need for expeditious and efficient approaches to assess the structural reliability of widespread aging bridge stocks. As such, a shift towards predictive maintenance measures is required. This paper addresses such pressing necessity for efficient methods to assess the structural state of aging bridges, centring its research question on developing a Decision Support System (DSS) that integrates an innovative reliability model with Markov Chain projections to prioritize bridge interventions. The proposed DSS effectively forecasts structural risks and computes a Priority Index to guide maintenance decisions. This tool empowers infrastructure managers with timely insights, enhancing bridge safety and optimizing resource allocation. The study's practical example underscores the DSS's applicability, demonstrating its capacity to streamline decision-making in real-world scenarios. By providing a sophisticated yet accessible approach to structural reliability assessment, this research lays the groundwork for future enhancements, including the incorporation of additional risk factors and refined calibration methods.

Optimal design of laminated thermoelectric cylindrical device based on neural network and thermo-electric-mechanical coupled model

The laminated thermoelectric cylindrical shell is favorable for solar thermal energy harvesting, but the thermo-electric-mechanical coupling field is relatively complex. An optimization design model of thermoelectric structure is required to promote its practical application. In this paper, based on variable separation method, thermo-electric-mechanical coupled model of laminated thermoelectric cylindrical device under thermal shock is constructed. Transient temperature field, thermal stress and power generation efficiency are obtained. Numerical results show that both power generation efficiency and thermal stress are affected by material parameters simultaneously. In order to analyze efficiency and structural reliability comprehensively, a fitness function is introduced in this paper. Based on the data which are collected from thermoelectric coupled model, a neural network is established and trained, which can predict and evaluate the performance of thermoelectric cylindrical shell based on the fitness function. The optimal material parameters for thermoelectric shells with high efficiency and low thermal stress are obtained. Combining neural network technique and the theoretical model developed in this paper, the computation time of numerical results can be reduced from 50 to 3 min, and this neural network optimization method has higher computational efficiency. These research results will provide guidance for the optimization design of cylindrical thermoelectric device.

Determination of equivalent crack length for voids in metallic alloys based on continuum damage mechanics modeling

Metals contain different kinds of spatial defects significantly reducing the fatigue performance. Accurate defect-based life prediction methods are required to improve structural integrity analysis and life design reliability. The present work developed a method to determine the equivalent crack length for defects based on continuum damage mechanics. The spatial effects of defects in the calculation of the equivalent crack were quantified and verified by fatigue tests. It is confirmed that the equivalent crack length of both slit and elliptic defects is affected not only by the defect geometry but also by the applied load. Based on the equivalent crack and combined with Paris’ law, the predicted fatigue lives of various slit holes under different loading orientations are within the double scatter band and are much more accurate than the conventional projection method.

New Psychometric Evidence of the Life Satisfaction Scale in Older Adults: An Exploratory Graph Analysis Approach.

The objective of the present study was to analyze the psychometric properties of a life satisfaction scale in older Peruvian adults using an exploratory graph analysis (EGA) approach. A total of 407 older adults aged between 60 and 95 years (M = 69.5; SD = 6.7) from three comprehensive elderly care centers (CIAMs) in Lima, Peru, were recruited. A non-probabilistic convenience sampling was used. The Satisfaction with Life Scale (SWLS) was analyzed using EGA with the Gaussian GLASSO model to assess its dimensionality and structural consistency. The relationship with other variables was analyzed using scales such as the GAD-7 and PHQ-9. The network structure of the SWLS indicates a single dimension. Additionally, network loadings (nodes) were examined, showing high values (>0.35) for most items except item 1, which had a moderate loading (>0.25). Structural reliability showed that a single dimension was identified 100% of the time. The post hoc CFA considering the unidimensional network structure obtained through EGA showed satisfactory fit (χ2/df = 3.48, CFI = 0.96, TLI = 0.92, SRMR = 0.02, RMSEA = 0.07 [90% CI 0.05, 0.08]). Finally, internal consistency reliability was acceptable (ω = 0.92). The SWLS measure is robust and consistent. These findings are a valuable reference for advancing research on aging in Peru, as they provide a practical, valid, and reliable measure.

A model-based deep learning framework for damage classification and detection in polycarbonate infused with AEROSIL under dynamic loading conditions

Composite 3D printing is a significant engineering application owing to its robustness, ability to achieve complex geometries, and ease of use. Polycarbonate, particularly when infused with AEROSIL, is an interesting thermoplastic and potential candidate for 3D printing with enhanced properties. The primary objective of this research is to develop a new model-based deep-learning framework to classify and detect damage in this material under dynamic loading conditions. To achieve this, a FASTCAM high-speed camera was placed in front of the SHPB test setup to capture dynamic damage. The test results were then used as label inputs for training the advanced deep learning algorithms, focusing on dense image recognition techniques for detailed damage analysis. The study involved a series of fully convolutional networks (FCNs), evaluating semantic segmentation with U-Net and instance segmentation with state-of-the-art frameworks such as YOLOv8 and Mask R–CNN. A comparative analysis revealed that deep learning models outperform traditional methods, providing efficient and accurate damage classification and detection. The U-Net model demonstrated the ability to recognize cubes and bars but was limited in detecting minor damage regardless of size. YOLO-V8, which specializes in case segmentation, achieved remarkable performance in detecting significant damage but struggled to accurately identify minor damage. By leveraging deep learning techniques, this study enables an efficient and accurate damage assessment, which is crucial for ensuring the reliability and safety of composite structures in various industries.

Development of Phased Array Ultrasonic Testing System Using Distributed Architecture

Abstract With more rigorous requirements for reliability and safety of structures and materials, high resolution and fast imaging have been urgent needs of Phased Array Ultrasonic Testing (PAUT). In order to improve the detection resolution and efficiency of complex structures and materials such as ultra-thick carbon steel and honeycomb plate, a PAUT system is proposed in this paper. The system uses a distributed architecture, which not only divides hardware resources, improves system performance, but also is scalable. PA, single- and multi-channel conventional UT, and TOFD (Time Of Flight Diffraction) modalities are combined, and PA modality is carried by the fully parallel non-multiplexed 128 PE (Pulse Echo) electronics with independently adjustable emitting and receiving parameters. In addition, the ultrasound analog front-end circuit is designed using discrete devices, providing more powerful analog performance than integrated chips. GTX (Gigabit Transceiver) and PCIe (Peripheral Component Interconnect Express) are applied to high-speed data transmission, providing raw ultrasound data. Characterization and verification results indicate that the system is ideal for challenging inspections in many industries.

Nonprobabilistic time-dependent reliability analysis for uncertain structures under interval process loads

In this paper, a novel nonprobabilistic analysis framework is proposed to evaluate the time-dependent reliability of uncertain structures under time-varying loads. Firstly, a novel uncertainty propagation method is developed by combining interval process integration and surrogate-based interval analysis and the correlation coefficient between responses of adjacent time steps is further analyzed. Subsequently, the nonprobabilistic time-dependent reliability is analyzed base on the first-passage theory and the established interval model. Unlike existing nonprobabilistic methods that consider time-invariant external loads, the proposed method applies an interval process to describe time-varying external loads, thereby offering a broader range of applicability. Compared to existing nonprobabilistic methods that consider time-varying loads, the proposed method establishes a more refined nonprobabilistic time-dependent reliability model based on the first passage theory, achieving higher accuracy. The effectiveness and superiority of the proposed method are validated through a numerical example and an engineering application.

Research on the configuration of electromechanical suspension based on inertial force attenuation structure

To address the critical challenges impeding the progress and utilization of electromechanical suspensions, this paper examines the negative consequences of inertial mass on the acceleration of the sprung mass and structural reliability issues. It then introduces and optimizes two novel suspension configurations featuring a series buffering and damping structure (SBDS). Subsequently, a two-degree-of-freedom electromechanical suspension model with SBDS is proposed and formulated. Utilizing the probability method, the paper also derives the vibration transmission characteristic formulas for the SBDS-integrated suspension. Theoretical analysis suggests that the integration of SBDS is advantageous in reducing the inertial force generated by the inertial mass in electromechanical suspensions. Additionally, the impact of parameter variations in SBDS on suspension characteristics is examined, providing theoretical guidance for the design of this structure. Building upon the proposed suspension theory incorporating the novel SBDS configuration, a butterfly spring buffer is employed to replace the connecting rod in the traditional electromechanical suspension design. Bench tests validate that the new electromechanical suspension configuration with SBDS addresses the issue of performance degradation in the high-frequency range due to inertial mass, thereby enhancing the overall performance of electromechanical suspensions and facilitating their further development and application.

A physics-informed deep learning approach for combined cycle fatigue life prediction

The prediction of the combined high and low cycle fatigue (CCF) life of welded components was valuable for the reliability of practical engineering structures. In this study, a physics-informed Grey-deep convolutional neural networks (DCNN) model was constructed and the model parameters was determined based on the fatigue behavior characteristics of CCF, which was proved perform well in the CCF life prediction of welded components. The predicted results indicated that the proposed model can realize reliable CCF life prediction with good accuracy (MAE = 0.49) and stability (RMSE = 0.44). Further, the proposed model also exhibited good prediction performance under different loading and geometry conditions, indicating the good universality of the proposed model. And the good universality represented that the proposed model had the potential to be applied in more engineering fields.

A study on the small failure probabilities of cylindrical composite hydrogen storage tanks using subset simulation

Although composite hydrogen tanks are becoming increasingly intriguing, a detailed structural reliability analysis is still lacking. The current landscape of analysis for pressurised multilayer cylinders, while rich in research, lacks tools to deal with the low probabilities of failure that govern the design of hydrogen tanks. This study presents a computational framework integrating the Subset Simulation method (SS) for assessing the failure probability of composite tanks used for hydrogen storage. To explore how randomness impacts design parameters, this study utilises a limit state equation derived from the thin-walled circumferential model of composite pressure tanks. Five random variables, each with varying coefficients of variation (COVs), are incorporated into the analysis. For comparison and validation purposes, two methods Monte Carlo simulations and FORM have been used. The analysis revealed that SS excels at estimating the low failure probabilities of composite hydrogen tanks, and showed also the feasibility and accuracy of SS in the prediction of burst pressure since good agreement was obtained between the probabilistic approach and the experimental results. Furthermore, the uncertainties related to internal pressure and composite thickness affect significantly the reliability of the structure and can lead to the shrinkage of the safety margin and the failure of the vessel.

Analysis of bi-directional functionally graded plate on an elastic foundation subjected to low velocity impact using the refined plate theory

This study investigates the mechanical response of a bi-directional functionally graded plate consisting of three distinct materials when subjected to low-velocity impact. Utilizing the New Refined Plate Theory, the research explores the mechanical behavior of the plate resting on a Pasternak elastic foundation, providing insights useful for engineering applications. The investigation examines various parameters, including initial impact velocity, projectile radius, volume fraction indices, and elastic foundation stiffness, and their effects on critical response characteristics such as contact force, indentation, lateral deflection, and projectile velocity. The theoretical predictions are validated through detailed comparisons with existing literature and numerical simulations, ensuring the reliability and applicability of the proposed methodology. This study introduces a novel approach using refined plate theory to analyze low-velocity impact on 2D-FGMs, providing practical insights for structural analysis. The findings deepen understanding of functionally graded materials and enhance the evaluation of impact-resistant structures. The research highlights the importance of diverse parameters in improving structural performance and reliability, relevant across aerospace, civil, and mechanical engineering. Detailed exploration shows that initial impact velocities and projectile radius enhance impact force and deflection, while volume fraction indices and foundation stiffness influence the dynamic response.

Development of a large stroke 3-DOF piezoelectric steering mirror for optical system

The steering mirrors and the focusing mechanisms are the key components of the optical system. Traditional steering mirrors driven by voice coil actuator or piezoelectric stack actuator have the disadvantages of short stroke, self-locking difficulty and poor structural reliability. These defects limit the performance of the steering mirrors and make it difficult to be compatible with the focusing mechanism. As a result, additional focusing devices are required in the optical system, which significantly increases the complexity of the system. To simplify the optical system, a large stroke three-degree-of-freedom (3-DOF) piezoelectric steering mirror (PSM) which can realize both light path adjustment and focus adjustment is proposed in this study. Two rotational degrees of freedom of the PSM are used to adjust the light path, and a translational degree of freedom is used to adjust the focus. The screwed-typed piezoelectric actuators (STPAs) are designed to drive the PSM by exciting the travelling wave. The feasibility of the operating principle and reliability of the proposed PSM have been verified by finite element simulation. A prototype of the PSM is fabricated and tested. The experimental results show that the PSM achieves rotational degrees of freedom around the x-axis, y-axis and translational degree of freedom along the z-axis with maximum ranges of ±0.17 rad, ±0.17 rad, and ±10 mm, respectively, meeting the application requirements of the optical systems. The resolutions of the two rotary motions of the PSM are 28 μrad, and 21 μrad, respectively, and that of the translational motion is 0.5 μm. Finally, the position adjustment and focusing operations of the image are demonstrated to validate the potential application of PSM in optical systems.

Detection of Debonding Defects in Carbon Fiber-Reinforced Polymer (CFRP)-Rubber Bonded Structures Based on Active Lamb Wave Energy Analysis.

Carbon fiber-reinforced polymers (CFRPs) are widely used in the fabrication of solid rocket motor casings due to their exceptional performance. However, the bonding interface between CFRP and viscoelastic materials (rubber) is prone to debonding damage during service and storage under complex environmental conditions, which poses a significant threat to the structural integrity and reliability of the engine. Existing nondestructive testing (NDT) methods, such as X-ray imaging, infrared thermography, and ultrasonic testing, although somewhat effective, exhibit significant limitations in detecting interfacial defects in deep or multilayered composite materials, particularly under the challenging conditions of service and storage. This study proposes an innovative method based on active Lamb wave energy analysis and introduces the Damage Evolution Factor (DEF), specifically designed to detect and evaluate interfacial debonding defects in CFRP-rubber bonded structures within solid rocket motors during service and storage. Through numerical simulations and experimental validation, we selected the A0 mode Lamb wave, which is more sensitive to interfacial damage, as the incident wave and excited it on the surface of the structure. Displacement time-history response signals at observation points under different damage models were extracted and analyzed, and DEF values were calculated. The results show that DEF values increase with the size of the interfacial debonding damage. Similar trends were observed in experimental studies, further validating the effectiveness of this method and demonstrating that DEF can be used for the quantitative evaluation of interfacial debonding defects in CFRP-rubber bilayer bonded structures.

Prediction method for re-damage behavior of the repaired CFRP bolted joint

Composite materials are widely used in various aircraft due to their high specific strength, stiffness, corrosion resistance, and robust design flexibility. However, when subjected to complex loading conditions, composite structures are susceptible to damage, necessitating prompt repair upon aircraft return. Analyzing the re-damage process of repaired structures is crucial for assessing aircraft structural reliability, lifespan, and determining whether a repeat flight is warranted. In this study, a prediction method was proposed for re-damage behavior through meso-modeling and energy analysis of the repaired composite bolted joint. First, this article established a meso-mechanical model for composite laminates, analyzed force transfer characteristics at the fiber − matrix interface, and derived equations for stress field. Additionally, a strain energy distribution model was developed for composite bolted repaired joint near fibers and repaired area, proposing a damage prediction method based on the strain energy criterion. This method could anticipate the location, type, sequence, and magnitude of damage. Finally, simulation calculations yielded mechanical characteristics of the repaired area under typical load conditions, while loading tests verified the feasibility of the prediction method. This study offered an effective means of predicting performance degradation in composite structures and provided a vital direction for optimal spacecraft structure repairing methods and parameters.

Psychometric evaluation of clinician- and caregiver-reported clinical severity assessments for individuals with CDKL5 deficiency disorder.

The CDKL5 Clinical Severity Assessment (CCSA) is a comprehensive, content-validated measurement tool capturing the diverse challenges of cyclin-dependent kinase-like 5 (CDKL5) deficiency disorder (CDD), a genetically caused developmental epileptic encephalopathy (DEE). The CCSA is divided into clinician-reported (CCSA-Clinician) and caregiver-reported (CCSA-Caregiver) assessments. The aim of this study was to evaluate the factor structure of these measures through confirmatory factor analysis (CFA) and evaluate their validity and reliability. Participants were recruited from the International CDKL5 Clinical Research Network to take part in an in-clinic CCSA-Clinician evaluation (n = 148) and/or complete the CCSA-Caregiver questionnaire (n = 198). CFA was used to determine domains, and factor loadings and validity were assessed. For the CCSA-Clinician, inter-rater reliability was assessed by nine CDD experienced clinicians via 14 pre-recorded evaluations. Eight clinicians re-viewed and re-scored the videos after 4 weeks to evaluate intra-rater reliability. The CCSA-Caregiver was completed on a second occasion by 34 caregivers after 2-4 weeks to assess test-retest reliability. CFA resulted in three domains for the CCSA-Clinician (motor and movement, communication, vision) and four domains for the CCSA-Caregiver (seizures, behavior, alertness, feeding), with good item loadings across both measures. Structural statistics, internal consistency, discriminant validity, and reliability were satisfactory for both measures, and scores were consistent between known groups. This study provides strong evidence that the CCSA measures are suitable to assess the clinical severity of individuals with CDD, supporting their use in clinical trials. Further evaluation of responsiveness to change in a longitudinal assessment is planned. Use may also be appropriate in similar DEEs but would require validation in those populations.

Correction of thermal airflow distortion in warpage measurements of microelectronic packaging structures via deep learning-based digital image correlation

The projected speckle-based three-dimensional digital image correlation method (3D-DIC) is being increasingly used in the reliability measurement of microelectronic packaging structures because of its noninvasive nature, high precision, and low cost. However, during the measurement of the thermal reliability of packaging structures, the thermal airflow generated by heating introduces distortions in the images captured by the DIC measurement system, impacting the accuracy and reliability of noncontact measurements. To address this challenge, a thermal airflow distortion correction model based on the transformer attention mechanism is proposed specifically for the measurement of thermal warpage in microelectronic packaging structures. This model avoids the oversmoothing issue associated with convolutional neural networks and the lack of physical constraints in generative adversarial networks, ensuring the precision of grayscale gradient changes in speckle patterns and minimizing adverse effects on DIC calculation accuracy. By inputting the distorted images captured by the DIC measurement system into the network, corrected images are obtained for 3D-DIC calculations, thus allowing the thermal warpage measurement results of the sample to be acquired. Through experiments measuring topography with customized step block specimens, the effectiveness of the proposed method in improving warpage measurement accuracy is confirmed; this is particularly true when captured images are affected by thermal airflow at 140 °C and 160 °C, temperatures commonly encountered in thermal reliability testing of packaging structures. The method successfully reduces the standard deviation from 9.829 to 5.943 µm and from 12.318 to 6.418 µm, respectively. The results demonstrate the substantial practical value of this method for measuring thermal warpage in microelectronic packaging structures.

Physics-informed neural network classification framework for reliability analysis

Reliability analysis is crucial for quantitatively evaluating structural safety amidst uncertainties, laying the foundation for reliability-based design optimization aimed at augmenting structural reliability and reducing economic expenditures, thereby offering substantial engineering benefits. However, performing reliability analysis on intricate structures, especially those requiring time-intensive finite element models, presents a formidable challenge. This study develops an innovative physics-informed neural network classification (PINNC) model to tackle this issue. In the PINNC model, the loss associated with the structural output state (i.e., safety or failed state) is defined as classification loss. Additionally, the structural output value (i.e., actual structural response) is treated as critical physical information, with its associated loss termed as physical loss. To facilitate a separate calculation of physical loss and classification loss, a parametric sigmoid activation function is used, establishing a link between the structural output value and the structural output state. The total loss is calculated as a weighted sum of both physical and classification losses. Unlike conventional neural network classification models that solely focus on classification loss, the PINNC model integrates both physical and classification losses, markedly improving the model’s efficacy in structural reliability analysis. Moreover, an adaptive framework is developed to incrementally include samples proximal to the limit state surface as new training data, thereby reinforcing the PINNC model’s computational precision. The effectiveness of the proposed PINNC framework in overcoming reliability analysis challenges is demonstrated through various applications.

Performance degradation evaluation of square steel tubes with local penetration damage

Steel tubular structures are susceptible to welding defects, cracking defects and environmental corrosion, which can negatively affect the stability and reliability of structure. This study tested the axial compression performance of twenty-six square steel tubular stubs with localized penetration damage simulated by artificial notch, considering the influence of notch length, orientation, and position. The test results showed that the failure mode of the specimens was dominated by the local buckling along the direction of the notch axis. The failure mode of the specimen with vertical notch showed notch opening, while those with horizontal notch showed notch closing, resulting in the secondary peak value in the load-displacement curve. The existence of local penetration damage reduced the ultimate strength and ductility of the tubular stubs. Compared to the ultimate strength of the specimens with middle notch, those with corner notch were weakened more, up to 27 %. Furthermore, the effects of notch dimension, position, orientation, and tube wall thickness on the axial bearing capacity of square steel tubular stubs are discussed by using a validated finite element model. Owing to the analysis, the ultimate strength of the stubs is reduced by increasing notch length, width and depth, whereas the reduction effect tends to be weakened as the notch orientation angle increases. The notch located in the corner is the most unfavorable for the axial compression performance of the square steel tubular stubs. A semi-empirical formula for evaluating the ultimate strength of square steel tubular stubs with local penetration damage is proposed by introducing the strength reduction factor. An artificial neural network-based ultimate strength prediction model is developed that can consider the effects of wide-range parameters, achieving high accuracy.