Variability Of Structural Parameters Research Articles

Improved hierarchical Bayesian modeling framework with arbitrary polynomial chaos for probabilistic model updating

Bayesian finite element model updating techniques have found widespread application in the structural health monitoring. The conventional Bayesian modeling framework (CBMF) identifies the posterior distribution of structural parameters using data from a single experiment. However, structural parameters exhibit variability due to changes in environmental or experimental conditions, an aspect overlooked by CBMF. In recent years, the hierarchical Bayesian modeling framework (HBMF) has gradually attracted attention. This framework incorporates an additional layer of hyperparameters and utilizes multiple sets of experimental data to describe the variability in structural parameters caused by changes in experimental conditions. Unfortunately, the increase in hyperparameters and data size leads the HBMF to face the dilemma of exceedingly high computational costs. To address this issue, this study proposes an improved hierarchical Bayesian modeling framework (IHBMF). To enhance the computational efficiency of IHBMF, the importance sampling method is employed to simplify the computation of the likelihood function for hyperparameters. Subsequently, a surrogate model based on arbitrary polynomial chaos expansion is introduced to further reduce the computational cost of IHBMF. The framework's accuracy and efficiency were validated through analysis of a simply supported beam and a steel pedestrian bridge. The results demonstrate that IHBMF not only delivers a more accurate quantification of structural parameter uncertainty than CBMF but also exhibits higher computational efficiency compared to HBMF, showcasing its superior capability in structural health monitoring applications.

Seismic Behavior of Colliding Buildings, Incorporating Soil-structure Interaction and Accounting for Variability in Structural Parameters, Soil Parameters, and Seismic Action

Pounding between buildings that are not sufficiently separated has been observed several times during earthquakes. This destructive impact may severely damage the structure and lead to its collapse. Although it is impossible to completely eliminate such losses, measures can be taken to minimize them. This article investigates the effect of the variability of structural parameters, soil parameters, and seismic action on the seismic response of two colliding buildings, taking the soil-structure interaction (SSI) into account. Two adjacent structures closely separated, modeled as inelastic lumped mass systems with different structural characteristics, were considered in this study. Both structures were modeled in the analysis using multi-degree-of-freedom (MDOF) systems, and the pounding was simulated using the modified linear viscoelastic model. The analysis was conducted in two cases: probabilistic analysis and deterministic analysis. Probability curves were established to analyze the effect of the variability of the parameters on the responses of the two colliding buildings. The comparison between the two analyses indicates that the probabilistic analysis is more precise than the deterministic analysis. It has been indicated that taking into account the variability of structural parameters, soil parameters, and seismic action is efficient in determining the realistic behavior of colliding buildings. Additionally, pounding is more critical in the case of buildings founded on very soft soil, followed by those on soft soil, then on hard soil, and finally on rocky soil.

Building portfolio seismic fragility analysis: incorporating building-to-building variability to carry out seismic fragility analysis for reinforced concrete buildings in a city

Predicting how future earthquakes will affect structural performance in a particular region is a challenge due to the unpredictable nature of earthquakes and inherent uncertainties in construction materials and the geometry of structures. This study considers building-to-building variabilities as inputs in an existing Response Surface Methodology (RSM) that are based on the Design of Experiment (DoE) technique to quickly determine the structural fragility of a region. As it is impractical to analyse each individual structure in a region in detail, this study addresses the issue by collecting a set of building parameters and using screening design to evaluate the most significant building parameters in the overall seismic performance. Instead of performing building-to-building analyses, the propagation of uncertainty in building-to-building variability can now be performed using a polynomial response surface metamodel. The metamodel is a function of the response of the building, and a set of significant parameters. This study aims to obtain the fragility of a collection of buildings with the help of the existing RSM by considering variability in structural parameters that can represent the overall structural geometric and material properties within a region. A case study is conducted for the collection of reinforced concrete (RC) buildings in the city of Silchar, located in Northeast India, which is one of the most seismically active regions of the country. The fragility curve developed with significant building parameters from RSM has been compared with that of the conventional method using Incremental Dynamic Analysis (IDA), and the match of the result confirms the accuracy of fragility assessment using RSM.

Powernet: SOI Lateral Power Device Breakdown Prediction With Deep Neural Networks

The breakdown performance is a critical metric for power device design. This paper explores the feasibility of efficiently predicting the breakdown performance of silicon on insulator (SOI) lateral power device using multi-layer neural networks as an alternative to expensive technology computer-aided design (TCAD) simulation. In this work, we propose the first breakdown performance prediction framework, PowerNet, for SOI lateral power devices, based on deep learning methods. The framework can provide breakdown location prediction and breakdown voltage (BV) prediction by utilizing a two-stage machine learning method. In addition, it demonstrates 97.67% accuracy on breakdown location prediction and less than 4% average error on the BV prediction compared with TCAD simulation. The proposed method can be used to measure changes in performance caused by random variability in structural parameters during manufacturing process, allowing designers to avoid unstable structural parameters and enhance design robustness. More importantly, it can significantly reduce the computational cost when compared with the TCAD simulation. We believe the proposed machine learning technique can significantly speedup the design space exploration for power devices, eventually reducing the overall product-to-market time.

Seismic behavior of R.C. structures including soil-structure interaction and soil variability effects

In the context of performance-based earthquake engineering (PBEE), a challenging task for structural engineers is to provide performance and risk assessment for structures or soil-structure interaction (SSI) systems. In order to fulfill this task successfully, all relevant sources of aleatory and epistemic uncertainties must be accounted for during the design process. Thus, proper methods are required for the study of uncertainty propagation from model parameters describing the structure, the soil, and the applied loads to structural responses by defining some performance limit states. The main goals of this article are to formulate an approximate procedure for analyzing SSI and to evaluate the relative importance of its effects, as well as the influence of the principal parameters involved (performance point, reduction factor, …). The N2 method is extended to determine the nonlinear response of a structure under seismic loading by including SSI effects in the design. Both aleatory and epistemic uncertainties are considered. Related to SSI analysis, several issues are studied, such as relative importance of soil parameters, relative foundation/soil stiffness ratio, regarding a specified aspect of the system response (e.g., response parameters). It was confirmed that the response of the structure not only depends on its dynamic characteristics and on the seismic excitation characteristics but also on the external environment surrounding the base of the structure, i.e. the interaction between the structure, the foundation and the soil. Furthermore the major influence in the randomness of the structural response comes from the randomness of the seismic action and the soil parameters and slightly from the variability of structural parameters.

Test–retest variability in structural parameters measured with glaucoma imaging devices

In addition to classical stereo-disc photography, various glaucoma imaging devices were developed in the last two decades to quantitatively measure and record glaucoma-related structural parameters of the eye. In determining whether or not the glaucomatous damage progressed from baseline and in estimating the number of test results' optimal frequency needed to confirm disease progression, information relating to the test-retest variability of measurement results provided by each imaging device is indispensable. Such information enables the clinician to apply these devices in practice. The test-retest variability of a system is usually estimated using the Bland-Altman analysis and by calculating the coefficient of variation (CV), intraclass correlation coefficient (ICC), and minimum detectable changes (MDC). The reported CV, ICC, and MDC values for glaucoma-related structural parameter measurement results of stereo-disc photographs, confocal scanning laser ophthalmoscopes, scanning laser polarimeters, time-domain optical coherence tomography (OCT), spectral-domain OCT (SD-OCT), anterior-segment OCT, and ultrasound biomicroscope are systematically reviewed in this manuscript, which will enable the clinician to interpret measurement results provided by each glaucoma imaging devices and thus be useful in practice. Although SD-OCT systems may be currently prevailing because of the volume of information provided and the relatively better test-retest variability, these systems need improvement in their test-retest variability measurement capabilities.

Variability in sepiolite: Diffraction studies

Twenty sepiolites of known composition from different origins were analyzed to quantify the variability in structural parameters and its possible relationships with composition and morphology. Morphology SEM analyses led to classify the sepiolites into several groups, beyond the two simple macroscopic or clay groups. X-ray powder diffraction with synchrotron light was used to discuss the variability of the a and b cell parameters with the nature of the cations and occupancy of the octahedral shell. Rietveld refinement using the ideal sepiolite model is performed on sepiolites at two temperatures: 225 °C (for zeolitically dehydrated sepiolite) and 25 °C (for hydrated ambient sepiolite). The latter permitted to locate ca. six molecules of the zeolitic H2O within the tunnels. A few samples were selected to evaluate the feasibility and potential of single-crystal diffraction methods: X-ray microdiffraction and electron diffraction. The macroscopic sepiolites gave well-structured and rich X-ray fiber diffraction patterns, in excellent agreement with ab initio simulations. High-quality single-crystal electron diffraction patterns for three axis zones are indexed and compared with simulations. The experimental and modeling results for X-ray microdiffraction and electron diffraction open a new path for quantitative crystallography on sepiolite and other fibrous clays from the sepiolite-palygorskite group.

Health Monitoring System (HMS) for structural assessment

As in any engineering application, the problem of structural assessment should face the different uncertainties present in real world. The main source of uncertainty in Health Monitoring System (HMS) applications are those related to the sensor accuracy, the theoretical models and the variability in structural parameters and applied loads. In present work, two methodologies have been developed to deal with these uncertainties in order to adopt reliable decisions related to the presence of damage. A simple example, a steel beam analysis, is considered in order to establish a liable comparison between them. Also, such methodologies are used with a developed structural assessment algorithm that consists in a direct and consistent comparison between sensor data and numerical model results, both affected by uncertainty. Such algorithm is applied to a simple concrete laboratory beam, tested till rupture, to show it feasibility and operational process. From these applications several conclusions are derived with a high value, regarding the final objective of the work, which is the implementation of this algorithm within a HMS, developed and applied into a prototype structure.

Health Monitoring System (HMS) for structural assessment

As in any engineering application, the problem of structural assessment should face the different uncertainties present in real world. The main source of uncertainty in Health Monitoring System (HMS) applications are those related to the sensor accuracy, the theoretical models and the variability in structural parameters and applied loads. In present work, two methodologies have been developed to deal with these uncertainties in order to adopt reliable decisions related to the presence of damage. A simple example, a steel beam analysis, is considered in order to establish a liable comparison between them. Also, such methodologies are used with a developed structural assessment algorithm that consists in a direct and consistent comparison between sensor data and numerical model results, both affected by uncertainty. Such algorithm is applied to a simple concrete laboratory beam, tested till rupture, to show it feasibility and operational process. From these applications several conclusions are derived with a high value, regarding the final objective of the work, which is the implementation of this algorithm within a HMS, developed and applied into a prototype structure.