Inverse Analysis Research Articles

Data assimilation by combining ABAQUS with ensemble Kalman filter and its application to geotechnical engineering

Geological parameters of soil exhibit spatial variability. Inverse analysis allows the acquisition of accurate spatial distributions of key geological parameters, which is crucial for structural safety assessment. In this study, an ensemble Kalman filter (EnKF) is employed in the context of data assimilation. Random fields are used as the initial input ensembles for the algorithm. The present study effectively integrates the ensemble Kalman filter with the numerical simulation software ABAQUS, enabling the inversion of parameter fields under various operating conditions. An in-house Python code script is developed to control ABAQUS for finite element computations and to obtain observations at target points. During the stepwise computation process, the algorithm can utilize newly acquired observations to accelerate the convergence of the parameter field to the true field. The effectiveness of the algorithm is validated, and the method is applied to a case study of double-tunnel excavation and a stepwise excavation analysis of a three-layered slope. The impact of the number of ensemble members and the ratio of the horizontal correlation scale to the vertical correlation scale of random fields on the effectiveness of updating the parameter field have also been investigated.

The Effect of Baricitinib and Corticosteroid Compared to that of Corticosteroid Monotherapy in Severely and Critically ill Patients with COVID-19: A Japanese Multicenter Inpatient Database Study.

BackgroundSystemic baricitinib and corticosteroids play important roles in treating severely and critically ill patients with coronavirus disease 2019 (COVID-19). However, the efficacy of the combination of baricitinib and corticosteroids compared to that of corticosteroid monotherapy in severely and critically ill hospitalized patients with COVID-19 remains unclear. MethodsWe analyzed severely and critically ill hospitalized patients with COVID-19 aged >18 years between January 1, 2020 and May 31, 2023, using a Japanese multicenter inpatient database. We performed propensity score matching to analyze the effect of the combination of baricitinib and corticosteroids within 2 days of hospital admission (combination group) on the 28-day and in-hospital mortality rates compared with those of corticosteroid monotherapy within 2 days of hospital admission (control group). Sensitivity analysis was performed using inverse probability weighting analysis and the generalized estimating equation method. ResultsThe eligible patients (n=7,433) were divided into a combination (n = 679) and a control group (n = 6,754). One-to-four propensity score matching analyses included 566 combination and 2,264 control group patients. There was no significant difference in 28-day (8.5% vs. 8.8%; risk difference, -0.3% [95% confidence interval, −2.9 to 2.3]) or in-hospital (11% vs. 10%; risk difference, 1.0 [−1.9 to 3.9]) mortality rates between 2 groups. The sensitivity analysis showed similar outcomes. ConclusionThis observational study, using a Japanese multicenter inpatient database, found that the combination of baricitinib and corticosteroid therapy did not improve the 28-day or in-hospital mortality rates in severely and critically ill patients with COVID-19 compared to corticosteroid monotherapy.

PNN Enhanced Seismic Inversion for Porosity Modeling and Delineating the Potential Heterogeneous Gas Sands via Comparative Inversion Analysis in the Lower Indus Basin

Seismic inversion has been in use for the last two decades to measure inverted impedances using an integrated data set approach. This research focuses on the application of multi-attribute seismic inversion and the geostatistical probabilistic neural network (PNN) approach for determining rock properties and litho-fluid classification in the Mehar-Mazarani Field of the Lower Indus Basin (LIB), Pakistan. The study compares five different inversion techniques, including model-based inversion (MBI), colored inversion (CI), linear sparse spike inversion (LSSI), band-limited inversion (BLI), and maximum likelihood sparse spike inversion (MLSSI). The inverted outputs, such as acoustic P-impedance (Zp), density (ρ), porosity (φ), and shale volume (Vsh), were analyzed in Paleocene and Cretaceous geological complex reservoirs to identify gas-bearing zones. The results indicated the existence of gas between 1630 and 1700 ms (ms) and corresponding depth ranges from approximately 3200 m up to 4200 m with varying thickness. Amongst the inversion techniques, MBI demonstrated greater accuracy, with inverted density volumes showing a strong correlation coefficient of 0.98 and the lowest root mean square error (RMSE) and relative error of 0.10 m/s * g/cc. A geostatistical PNN approach was employed to estimate variations in Vsh and φ within the sand reservoir. MBI again yielded more reliable results, with a strong correlation between the measured and inverted attributes. High φ and low Vsh were observed in predetermined low-impedance zones. Overall, MBI is proven to be the most accurate and reliable technique, providing clear identification of the gas occurrence. This research highlights the effectiveness of seismic inversion, particularly the application of MBI, in determining rock properties and identifying gas-bearing zones within the Mehar-Mazarani gas field.

Hybrid adaptive moment estimation based performance measure approach for complex reliability-based design optimization

The complexity of engineering problems often poses challenges, especially in reliability-based design optimization, where the nonlinearity of the performance function is not known in advance. The substantial costs and oscillating results significantly impede the engineering application for highly nonlinear problems. To address this issue, a hybrid adaptive moment estimation (HAME) method is proposed in this work. Given that the performance measure approach is heavily influenced by the gradient of performance function, this method employs the statistical moment information of the gradient, thereby accelerating the search for the minimum performance target point. By combining gradient mean and modified chaos control as new search directions, oscillation phenomena are resolved. To filter the aggregated gradient calculations, the gradient variance and state variable are introduced to approximate the gradients for weakly nonlinear performance functions or in later iterations by the dynamic identification of the nonlinearity of the performance function. The proposed method eliminates the limitation of taking the upper bound for the chaos control factor and addresses the balance issue between efficiency and robustness. The advantages of the proposed method are demonstrated through inverse reliability analysis problems, several highly nonlinear classic RBDO examples, and engineering applications of the crank rod mechanism of an engine.

Female genital prolapse and risk of psychiatric disorders: A two-sample Mendelian randomization analysis

BackgroundThere is a growing body of evidence suggests a strong link between female genital prolapse (FGP) and mental health. However, the causal relationship between FGP and psychological disorders remains unclear. ObjectivesBidirectional Mendelian Randomization (MR) analysis has been applied to investigate the potential impact of FGP on the risk of seven common psychiatric disorders. MethodsThe two-sample MR analysis was conducted using genetic instruments such as Inverse variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode from the genome-wide association study (GWAS) summary data in European populations. In addition, the Cochrane's Q test, MR-Egger intercept test, MR pleiotropy residual sum and outliers (MR-PRESSO) test and leave-one-out analysis were employed to assess the sensitivity and heterogeneity. ResultsThe MR results revealed that FGP exhibited a potential marginal protective effect on bipolar disorder (BD) (odds ratio(OR) = 0.92, 95%confidence interval (95%CI: 0.85–0.99, P = 0.03) as well as schizophrenia(OR = 0.91, 95%CI:0.85–0.98, P = 0.01). Nevertheless, there was no causal correlation between genetically predicted FGP and obsessive compulsive disorder (OCD) (OR = 0.98, 95%CI:0.80–1.20, P = 0.84),depression (broad) (OR = 1.00, 95%CI:0.99–1.01, P = 0.76), major depression(OR = 0.98, 95%CI:0.94–1.03, P = 0.43), anxiety disorders (OR = 1.00, 95%CI:0.94–1.07,P = 0.97) and post-traumatic stress disorder(PTSD) (OR = 1.18, 95%CI:0.88–1.57,P = 0.27),respectively. In addition, BD was found to have a potential significant influence on FGP in the inverse MR analysis (OR = 0.83, 95%CI:0.72–0.97, P = 0.02). No significant heterogeneity or horizontal pleiotropy detected, and the results were deemed stable based on sensitivity analysis and leave-one-out test . LimitationsThere are shortcomings such as data limitations, population bias, potential pleiotropy, and stratified analysis. ConclusionsWhile there is potential causal relationship between FGP and BD or schizophrenia, it does not exhibit any correction with OCD, depression (broad), major depression, anxiety disorders and PTSD among European populations.

Experimental and analytical investigations on debonding response of embedded through-section ribbed GFRP bar-to-concrete joints at elevated temperatures

Embedded Through-Section (ETS) technique has gained a great deal of increasing interest in the shear strengthening of existing reinforced concrete members, which is based on using fibre-reinforced polymer (FRP) bars installed through predrilled holes into concrete via adhesive. One of the key issues associated with the bonding of ETS FRP bars to concrete is the debonding of FRP bar-to-concrete interfaces which leads to premature and brittle failure of strengthened members, particularly under elevated temperatures. However, little research has been conducted on the influence of elevated temperatures on the bond behavior between ETS FRP bars and concrete. This paper presents experimental and analytical evaluations of the bond response of ETS-ribbed glass FRP (GFRP) bar-to-concrete joints subjected to elevated temperatures. A total of 90 pullout specimens were tested to quantify the influences of the temperature, bar diameter, and concrete strength on the failure mode, load response and bond strength. An analytical model to reproduce the full-range debonding process of ETS-GFRP bar-to-concrete interfaces was presented, which allowed simultaneously considering both long and short embedded lengths, interfacial friction, and snap-back behavior. The load-slip response, stress field, and peak load were solved analytically, resulting in a closed-form solution for the critical length for snap-back and a non-closed-form solution for the effective embedment length. After an inverse analysis procedure was proposed for determining the bond parameters, the accuracy of the analytical solution was verified through comparisons with the self-conducted test results and existing experimental data. A new temperature-dependent bond strength model was also developed. The findings revealed a remarkable decline in the bond strength of up to 92.3 % as the temperature increased from 20 °C to 160 °C, with about 60–70 % of this degradation occurring close to the glass transition temperature (Tg) of the adhesive. At temperatures well above Tg, the bond strength decreased with the bar diameter and displayed insignificant improvement with higher concrete strength. The results also demonstrated that the proposed analytical approach and bond strength model can predict the bond response of ETS ribbed GFRP bars embedded in concrete with reasonable accuracy.

Comparison of perioperative bleeding risk between direct oral anticoagulants in transurethral resection of prostate.

To evaluate the perioperative morbidity and mortality associated with direct oral anticoagulants (DOACs) and warfarin for patients receiving transurethral resection of prostate (TURP). This was a single-centre, retrospective cohort analysis of patients who underwent TURP for benign prostate hyperplasia from April 2019 to December 2023. The primary objective was to evaluate the perioperative bleeding and thromboembolic risk between anticoagulated (AC) vs no-AC patients. The secondary objective was to evaluate perioperative bleeding and thromboembolic risk between different formulations of DOACs. Patient demographics, prior treatment, prostate size, baseline bleeding risk, and operative details were collected. Bleeding and thromboembolic-related morbidity were captured within a 3-month postoperative period. Perioperative management of AC therapy was recorded, and all patients had their AC therapy withheld. Cohort characteristic between the AC vs no-AC, and DOAC groups were analysed with two-sided t-test, and chi-square test. Further logistic regression analyses were carried out to identified significant variables between the groups. These significant variables were used for adjustment in inverse probability-weighted treatment effect analysis to evaluate bleeding risk. There were 629 patients in the cohort, and 113 (18%) patients were receiving AC therapy. The AC patients were at 1.6 times statistically significant increased risk of acute bleeding, and 11 times increased risk of prolonged haematuria for >14 days. When compared to apixaban, patients on rivaroxaban conferred a statistically significant increased risk of acute bleeding by 2.21 times. Patients receiving AC therapy had a statistically significant increased risk of stroke in the perioperative setting (no-AC vs AC: 0.4% vs 2.7%, P = 0.01). This is the first study to evaluate risk of bleeding for TURP patients receiving DOACs. The AC patients are more likely to experience haematuria and stroke in the perioperative period despite withholding therapy. Apixaban appears to cause less bleeding-related complications than rivaroxaban.

Non-Stationary ETAS Model: How It Works for External Forcing

Abstract The stationary epidemic-type aftershock sequence (ETAS) model is based on the important empirical laws in aftershock statistics with a self-similar feature, and is therefore useful for the statistical analysis of many common earthquake occurrence series. However, because earthquake catalogs become richer, the non-stationarity arising from geophysical heterogeneity becomes more pronounced. This article discusses the utility of a model that assumes time dependence of the first two ETAS parameters, which are sensitive to the short-term prediction. The inversion analyses of the non-stationary ETAS model in this article show heuristic results for swarm earthquakes and complex mainshock–aftershock-type seismicity. The case studies demonstrate how these parameters change quantitatively in swarm seismicity associated with slow slips, increases in pore-fluid pressure such as magma and hydrothermal fluids, stress changes associated with an earthquake motion, and interseismic-induced effects due to geological properties.

Interpretable Structural Evaluation of Metal-Oxide Nanostructures in Scanning Transmission Electron Microscopy (STEM) Images via Persistent Homology.

Persistent homology is a powerful tool for quantifying various structures, but it is equally crucial to maintain its interpretability. In this study, we extracted interpretable geometric features from the persistent diagrams (PDs) of scanning transmission electron microscopy (STEM) images of self-assembled Pt-CeO2 nanostructures synthesized under different annealing conditions. We focused on PD quadrants and extracted five interpretable features from the zeroth and first PDs of nanostructures ranging from maze-like to striped patterns. A combination of hierarchical clustering and inverse analysis of PDs reconstructed by principal component analysis through vectorization of the PDs highlighted the importance of the number of arc-like structures of the CeO2 phase in the first PDs, particularly those that were smaller than a characteristic size. This descriptor enabled us to quantify the degree of disorder, namely the density of bends, in nanostructures formed under different conditions. By using this descriptor along with the width of the CeO2 phase, we classified 12 Pt-CeO2 nanostructures in an interpretable way.

Inverse analysis of soil hydraulic parameters of layered soil profiles using physics‐informed neural networks with unsaturated water flow models

AbstractInformation about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and the coupled movement of chemicals and heat at the field scale using a process‐based model. Physics‐informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh‐free solution, can be used to inversely estimate soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and α and n of the Mualem–van Genuchten model directly within layered soil profiles by predicting changes in the pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, time range used in error computations, and number of samples used to assess the physical constraint, was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in the pressure head and the three soil hydraulic parameter distributions within the layered soil were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.

Predictive numerical model for simulation of fiber-reinforced concrete beams in bending and inverse parametric analysis

Determining material parameters is essential for understanding the physical properties of fiber-reinforced concrete (FRC), enhancing the design process, and optimizing costs. This paper presents a deterministic numerical model of FRC beams under three-point bending tests (TPBT), with an emphasis on the failure analysis. This model aims to replicate TPBT and address the challenge that comes with direct measurements of FRC parameters, especially in the context of failure mechanisms. It integrates analytical solutions and explicit parameters to describe the bending and failure behavior, while mitigating computational demands in parameter identification due to the complex heterogeneous nature of the material. Laboratory tests were conducted to record key parameters and served as a foundation for the model development and validation of its predictive capabilities. The generated simulated data from the predictive numerical model were then utilized as input for an inverse analysis based on the Levenberg–Marquardt method. The inverse analysis demonstrated high accuracy, with parameter convergence typically within 3 to 7 iterations and errors reduced to less than 2%. Model verification was performed by comparing the results obtained from the inverse analysis with the data obtained from laboratory tests, confirming the model’s effectiveness in accurately predicting FRC failure behavior.

Primary immunodeficiency-related genes and varicella-zoster virus reactivation syndrome: a Mendelian randomization study.

Currently, evidence regarding the causal relationship between primary immunodeficiency-related genes and varicella-zoster virus reactivation syndrome is limited and inconsistent. Therefore, this study employs Mendelian randomization (MR) methodology to investigate the causal relationship between the two. This study selected 110 single-nucleotide polymorphisms (SNPs) of primary immunodeficiency-related genes as instrumental variables (IVs). Genetic associations of primary immunodeficiency-related genes were derived from recent genome-wide association studies (GWAS) data on human plasma protein levels and circulating immune cells. Data on genes associated with varicella-zoster virus reactivation syndrome were obtained from the GWAS Catalog and FINNGEN database, primarily analyzed using inverse variance weighting (IVW) and sensitivity analysis. Through MR analysis, we identified 9 primary immunodeficiency-related genes causally associated with herpes zoster and its subsequent neuralgia; determined causal associations of 20 primary immunodeficiency-related genes with three vascular lesions (stroke, cerebral aneurysm, giant cell arteritis); revealed causal associations of 10 primary immunodeficiency-related genes with two ocular diseases (retinopathy, keratitis); additionally, three primary immunodeficiency-related genes each were associated with encephalitis, cranial nerve palsy, and gastrointestinal infections. This study discovers a certain association between primary immunodeficiency-related genes and varicella-zoster virus reactivation syndrome, yet further investigations are warranted to explore the specific mechanisms underlying these connections.

Effects of spatial microstructure characteristics on mechanical properties of dual phase steel by inverse analysis and machine learning approach

This work aims to investigate complex relationship between microstructure characteristics and mechanical properties of dual phase (DP) steel through an inverse analysis based on Markov chain Monte Carlo (MCMC) method combined with meso-scale material modelling. In this framework, a machine learning approach as surrogate model was developed, in which support vector regression (SVR) and artificial neural network (ANN) were trained using results from representative volume element (RVE) simulations coupled with damage model, which were previously calibrated with experimental data of commercial DP steel grades. Moreover, specific microstructure descriptors including Moran’s index, martensite band index and martensite orientation were proposed for representing effects of spatial distributions of martensitic phase. As a result, inverse predictions of microstructure characteristics of DP steels for achieving defined yield strength, tensile strength, uniform elongation and toughness were presented. The inverse analysis could solve the non-uniqueness of structure–property relationships of steel, whereby significances of dispersed structures and aligned martensite bands were highlighted in details. The approach fairly dealt with multi-target optimization and high dimensional problem, which can be further applied as a guideline for designing DP microstructures with enhanced mechanical properties.

Numerical Modelling of Corrugated Paperboard Boxes

Numerical modelling of corrugated paperboard is quite challenging due to its waved geometry and material non-linearity which is affected by the material properties of the individual paper sheets. Because of the complex geometry and material behaviour of the board, there is still scope to enhance the accuracy of current modelling techniques as well as gain a better understanding of the structural performance of corrugated paperboard packaging for improved packaging design. In this study, four-point bending tests were carried out to determine the bending stiffness of un-creased samples in the machine direction (MD) and cross direction (CD). Bending tests were also carried out on creased samples with the fluting oriented in the CD with the crease at the centre. Inverse analysis was applied using the results from the bending tests to determine the material properties that accurately predict the bending stiffness of the horizontal creases, vertical creases, and panels of a box under compression loading. The finite element model of the box was divided into three sections, the horizontal creases, vertical creases, and the box panels. Each of these sections is described using different material properties. The box edges/corners are described using the optimal material properties from bending and compression tests conducted on creased samples, while the box panels are described using the optimal material properties obtained from four-point bending tests conducted on samples without creases. A homogenised finite element (FE) model of a box was simulated using the obtained material properties and validated using experimental results. The developed FE model accurately predicted the failure load of a corrugated paperboard box under compression with a variation of 0.1% when compared to the experimental results.

Terahertz spectroscopy and effective medium theory for thickness measurement of adhesive bonds

This study deals with the characterization of multilayer adhesive structures via terahertz waves, particularly focusing on inversion analysis of the adhesive layer thickness. Terahertz-time-of-flight (THz-TOF), sensitive to the dielectric properties of materials, serves as an excellent device for non-destructive evaluation. Further, the transfer matrix method is introduced to simulate THz propagation through layered materials with various optical properties. An improved model iteration approach and particle swarm optimization algorithm are employed to effectively determine the adhesive layer thickness. This methodology utilizes effective medium theory (EMT), specifically for adhesive penetration into the cushion, thereby enhancing the thickness measurement accuracy. Among the various EMT models, the Lorentz-Lorenz (L-L) model is commonly regarded as the most effective one. An objective function based on combining the Pearson correlation coefficient and the root mean square error is proposed to refine the iterative inversion process. The results of this approach have been benchmarked with traditional THz-TOF calculations and computed tomography imaging, revealing that the proposed methodology is consistent with CT findings and outperforms conventional THz-TOF calculations.

Piezoelectric Impedance-based Structural Damage Identification Empowered with Tunable Circuitry Integration and Multi-objective Optimization based Inverse Analysis

Abstract The piezoelectric impedance-based technique is increasingly recognized for its promise in structural health monitoring and damage identification. Built upon their self-sensing actuation capability, piezoelectric transducers can be integrated to host structures to acquire the system-level impedance information in high frequency range with small wavelength. Furthermore, the frequency-sweeping harmonic excitations in impedance measurements lead to the potential for model-based inverse identification of damage location and severity. A major challenge in damage identification, however, is that the inverse analysis is generally underdetermined, as the measurement information may not be adequate to yield a unique solution. In this research, a new methodology of tunable sensing in conjunction with multi-objective optimization inverse analysis is established. Taking advantage of the two-way electromechanical coupling of piezoelectric transducers, tunable inductance is integrated into the measurement circuit. For the same damage scenario, by tuning the inductance to a series of values, a family of impedance measurements can be acquired. Meanwhile, the inverse analysis is cast into a multi-objective optimization problem, aiming at minimizing the difference between measurement and model prediction and achieving sparsity in damage index vector. A Q-learning based multi-objective particle swarm optimization is synthesized to reach a small yet diverse solution set. We report the circuitry integration details as well as the algorithm enhancement with systematic case investigations. It is validated that the new methodology with enriched measurement can produce smaller solution set encompassing the true damage scenario, thereby providing vital information for diagnoses and prognosis.

α-Amylase inhibition and antioxidative activities of Marasmius tax sp extract: A combined in vitro and in silico exploration

Marasmius tax sp, belonging to the Marasmiaceae family, is a mushroom species that demands extensive study of its medicinal values with scientific evidence. Mycochemical profiling of methanolic extract of Marasmius sp was performed with Gas Chromatogram-Mass Spectroscopy (GC-MS) and presence of eighteen organic compounds were confirmed. The extract demonstrated promising results in terms of its ability to inhibit α-amylase and scavenge DPPH free radicals, indicating its potential as an antidiabetic and antioxidant. The anti-diabetic property of the mycochemicals was further explored by employing inverse molecular docking analysis for the identified mycochemicals against 18-diabetes associate targets. The inverse docking analysis showed four mycosterol compounds with a strong binding affinity against all the targets, while all 18 compounds displayed considerable binding scores (-6.0 to −10.0 kcal/mol) against three crucial targets responsible for the mode of antidiabetic effect, suggesting the extract to have a multi-target, multi-drug effect in treating diabetes. All the identified mycochemicals were evaluated for their drug-likeness, pharmacokinetics, adverse side effects, and bioactivity. Out of the 18 molecules, 17 were found to be bioactive, with 5 of them showing no predicted adverse effects. Further, the structure-property relationship of the mycosterols have been explored using Density Functional Theory (DFT) based calculations. Frontier molecular orbitals, electrostatic potential surfaces and global descriptor analysis were carried out to understand the stability and chemical reactivity of the selective mycochemcials.

The causality of atrial fibrillation on frailty index: A Mendelian randomization study.

Prior epidemiological research has indicated a possible association between atrial fibrillation (AF) and frailty status. Our study used Mendelian randomization to estimate its causality. The genome-wide association studies for AF were utilized as the exposure for individuals included in the UK Biobank (n = 463,010) and publicly available summary statistics data sets of genome-wide association studies meta-analyses for frailty index in individuals of European descent (n = 175,226) was used as the outcome. The inverse variance weighting method was utilized to evaluate causality. To further confirm the reliability of the results, sensitivity analyses were conducted. The inverse variance weighting analysis indicated that the presence of AF was found to be statistically linked to an increased risk of frailty (odds ratio = 3.017, CI: 1.106-8.232, P = .031). MR-Egger intercept test indicated no pleiotropy (Egger intercept = .002, P = .808). The leave-one-out method indicated that the individual SNPs did not have an impact on the robustness of the findings. The research implies a causal relationship between AF and frailty. Early detection and timely intervention of AF can control the occurrence of frailty.

Tensile properties of steel fiber reinforced recycled concrete under bending and uniaxial tensile tests

It is important to study the tensile behavior of steel fiber reinforced recycled concrete (SFRRC) for the reliability of structural design. This paper investigates the effects of different mixture systems, including full-NA, full-RCA, full-RFA, full-RA, and full-RA+10%RP, on the tensile behavior of recycled concrete through uniaxial tensile and three-point bending tests. In addition, the effects of the volume content of steel fiber on tensile behavior are investigated. The uniaxial tensile strength and stress-strain curves of SFRRC were tested and the load-deflection, P-CMOD, double K fracture parameter and fracture energy under flexural tensile load were measured. The results show that the incorporation of recycled materials may negatively affect the tensile strength of recycled concrete. Compared with natural concrete, the uniaxial tensile strength decreased by 28.4%–50.2 % and the flexural tensile strength decreased by 17%–42.52 %. In addition, tensile properties, such as initial crack stress, peak strain, double K fracture toughness, and fracture energy, will also decline. The effect of the volume content of steel fibers on the tensile properties of recycled concrete with different mixed systems is analyzed. The steel fiber changes the tensile cracking mode of recycled concrete. When the content of steel fiber exceeds 1 %, the SFRRC of different mixed systems has the phenomenon of deflection hardening. When the fiber content is 2 %, the uniaxial tensile strength is increased by 13.83%–69.07 %. Contrasting with the control group, the recycled concrete group containing 2 % steel fiber showed enhanced fracture energy, which was increased by 15–23 times with different mixture systems. Uniaxial tensile constitutive models of SFRRC have been established under different mixture systems. The tensile stress-strain response of SFRRC was obtained indirectly by inverse analysis. It has been found that flexural tensile tests overestimate the post-crack properties of materials, and this phenomenon is more obvious in recycled concrete. This research is of great significance for the structural design of SFRRC.

Bayesian inference of the spatial distribution of steel corrosion in reinforced concrete structures using corrosion-induced crack width

Observations of corrosion-induced crack widths offer crucial information about the corrosion states of steel reinforcements in reinforced concrete (RC) structures, enabling a cost-effective method for inferring corrosion states through inverse analysis. However, the uncertainty associated with the relationship between corrosion-induced cracking and steel weight loss necessitates a probabilistic inference method, especially when considering the spatial distributions of steel weight loss, which provides important information to estimate the load-bearing capacity loss of corroded RC structures. This paper proposes a Bayesian framework to infer the steel weight loss distribution in RC structures based on the observed corrosion-induced crack width. To reduce the dimensions of the Bayesian inference, a Karhunen-Loève transform is applied to extract the principal distribution features of the steel weight loss. The forward model of the Bayesian inference adopts a data-driven sequence-to-sequence transduction approach to predict corrosion-induced crack width from steel weight loss. This model incorporates a novel nonlinear convolution kernel for input encoding and a sparse polynomial chaos expansion for decoding, which proves more accurate and efficient than finite element simulations. The Hamiltonian Markov chain Monte Carlo (HMCMC) sampler is used to efficiently sample from the posterior distribution of the Bayesian inference. The case study of the proposed method demonstrated that Bayesian inference provides robust range estimation for the steel weight loss distribution, with its 95% confidence interval encompassing most observations. Additionally, the method efficiently inferred high-dimensional steel weight loss sequences up to 61 dimensions, taking advantage of the dimension reduction technique and the gradient-informed HMCMC sampler.