Search Results (117)

Accurate seismic performance assessment of flexure-shear critical reinforced concrete (RC) columns necessitates precise hysteresis modeling that captures their distinct cyclic characteristics—particularly pronounced strength degradation, stiffness deterioration, and pinching effects. However, existing hysteresis models for such circular RC columns fail to comprehensively characterize these coupled cyclic degradation mechanisms under repeated loading. This study develops a novel hysteresis model explicitly incorporating three key mechanisms: (1) directionally asymmetric strength degradation weighted by hysteretic energy, (2) cycle-dependent pinching governed by damage accumulation paths, and (3) amplitude-driven stiffness degradation decoupled from cycle count, calibrated and validated using 14 column tests from the Pacific Earthquake Engineering Research Center (PEER) structural performance database. Key findings reveal that significant strength degradation primarily manifests during initial loading cycles but subsequently stabilizes. Unloading stiffness degradation demonstrates negligible dependency on cycle number. Pinching effects progressively intensify with cyclic advancement. The model provides a physically rigorous framework for simulating seismic deterioration, significantly improving flexure-shear failure prediction accuracy, while parametric analysis confirms its potential adaptability beyond tested scenarios. However, applicability remains confined to specific parameter ranges with reliability decreasing near boundaries due to sparse data. Deliberate database expansion for edge cases is essential for broader generalization. Full article

The shear strength of rock discontinuities is a critical parameter in rock engineering projects for assessing the safety conditions of rock slopes or concrete dam foundations. It is primarily controlled by the frictional contribution of rock texture (basic friction angle), the roughness of discontinuities, and the applied normal stress. While proper testing is essential for accurately quantifying shear strength, engineering geologists and engineers often rely on published historical databases during early design stages or when test results show significant variability. This paper serves two main objectives. First, it intends to provide a comprehensive overview of the basic friction angle concept from early years until its emergence in the Barton criterion, along with insights into distinctions and misunderstandings between basic and residual friction angles. The other, given the influence of the basic friction angle for the entire rock joint shear strength, the manuscript offers an extended database of basic friction angle values. Full article

The drilling and blasting method is commonly employed for the rapid demolition of outdated buildings by destroying key structural components and inducing progressive collapse. The residual bearing capacity of these components is governed by the deformation morphology of the longitudinal reinforcement, characterized by bending deflection and exposed height. This study develops and validates a finite element (FE) model of a reinforced concrete (RC) column subjected to demolition blasting. By varying concrete compressive strength, the yield strength of longitudinal reinforcement, the longitudinal reinforcement ratio, and the shear reinforcement ratio, 45 FE models are established to simulate the post-blast morphology of longitudinal reinforcement. Two databases are created: one containing 45 original simulation cases, and an augmented version with 225 cases generated through data augmentation. To predict bending deflection and the exposed height of longitudinal reinforcement, artificial neural network (ANN) and random forest (RF) models are optimized using the hunter–prey optimization (HPO) algorithm. Results show that the HPO-optimized RF model trained on the augmented database achieves the best performance, with MSE, MAE, and R² values of 0.004, 0.041, and 0.931 on the training set, and 0.007, 0.057, and 0.865 on the testing set, respectively. Sensitivity analysis reveals that the yield strength of longitudinal reinforcement has the most significant impact, while the shear reinforcement ratio has the least influence on both output variables. The partial dependence plot (PDP) analysis indicates that the ratio of shear reinforcement has the most significant impact on the deformation of longitudinal reinforcement. Full article

Shear failure in reinforced concrete (RC) beams is abrupt and brittle, occurs without warning, and leaves no opportunity for internal stress redistribution. Despite the critical need for accurate shear strength assessment, existing methods vary widely across regions, leading to inconsistencies in practice. This study presents a unified shear strength equation for non-prestressed rectangular RC beams without stirrups, developed for simplicity and broad applicability. The model requires only basic geometric and material properties and applies to both shear-slender and non-shear-slender beams. It was formulated using a data-driven approach that combines an extensive experimental database collected up to 2007 with advanced computational techniques, including Artificial Neural Networks, Generative Adversarial Networks, and Bayesian optimization. The proposed equation was evaluated against established shear provisions, such as ACI 318-25 and CSA A23.3-24, and benchmarked with an experimental database. The results show that the model improves prediction accuracy, reduces uncertainty, and provides a more consistent method for shear strength assessment. The robustness of the equation was further confirmed through additional experimental database gathered after 2007, demonstrating strong agreement with test results and lower prediction uncertainty than current code provisions. These findings support the potential adoption of the proposed equation in engineering practice. Full article

Objectives: The widespread adoption of three-dimensional (3D)-printed resins in restorative dentistry has introduced significant challenges in establishing strong and lasting bonds with resin-based cements. Despite the development of numerous surface treatment techniques designed to improve adhesion, a clear consensus on the most effective approach remains elusive. This systematic review and meta-analysis critically examined the impact of various surface treatment protocols on the bond strength of 3D-printed resins. By comparing treated versus untreated surfaces, the study aimed to determine the most reliable strategies for enhancing adhesion, ultimately offering evidence-based guidance to inform clinical decision-making. Methods: This review identified relevant studies through a comprehensive search of MEDLINE via PubMed, Web of Science, Scielo, Scopus, and EMBASE databases, supplemented by manual reference checks, to identify in vitro studies published up to February 2025. Studies assessing the bonding of 3D-printed resins following various surface treatments and bonding protocols were included. Data on bond strength outcomes, such as shear bond strength, microtensile bond strength, and microshear bond strength, were extracted. Data extraction included study details, type of 3D-printed resin and printing technology, surface treatment protocols, bond strength testing methods, storage conditions, and results. The quality of included studies was assessed using the ROBDEMat tool. Meta-analyses were performed using the Review Manager Software (version 5.4, The Cochrane Collaboration, Copenhagen, Denmark), with statistical significance set at p < 0.05. Statistical heterogeneity among studies was evaluated using the Cochran Q test and the I² inconsistency test. Results: Nine studies met the criteria for qualitative analysis, with eight included in the meta-analysis. The findings revealed that surface treatment protocols significantly enhanced the immediate bond strength to 3D-printed resins (p = 0.01), with only sandblasting and silane demonstrating a statistically significant effect (p < 0.007). Similarly, after aging, surface treatments continued to improve bond strength (p = 0.01), with sandblasting and hydrofluoric acid being the only methods to produce a significant increase in bond strength values (p < 0.001). Conclusions: This meta-analysis underscores the importance of combining mechanical and chemical surface treatments, especially sandblasting and silane application, to achieve reliable and durable bonding to 3D-printed resins. Full article

This study presents an innovative data-driven methodology to model the hardening behavior of sheet metals across a broad strain range, crucial for understanding sheet metal mechanics. Conventionally, true stress–strain data from such tests are used to analyze plastic flow within the pre-necking regime, often requiring additional experiments to inverse finite element methods, which demand extensive field data for improved accuracy. Although digital image correlation offers precise data, its implementation is costly. To address this, we integrate experimental data from standard tensile tests with a machine-learning approach to estimate the flow curve. Subsequently, we conduct finite element simulations on uniaxial tensile tests, using materials characterized by the Swift constitutive equation to build a comprehensive database. Loading force-gripper displacement curves from these simulations are then transformed into input features for model training. We propose and compare three models—Models A, B, and C—each employing different input feature selections to estimate the flow curve. Experimental validation including uniaxial tensile, plane strain, and simple shear tests on the DP590 and DP780 sheets are then carefully considered. Results demonstrate the effectiveness of our proposed method, with Model C showing the highest efficacy. Full article

In any rock engineering project, the uniaxial compressive strength (UCS) is one of the most relevant parameters to be determined as it is used for a variety of purposes. Traditionally, the UCS of a rock sample is obtained by carrying out a uniaxial test on a rock core. The UCS can also be estimated indirectly by correlating it with different parameters such as the pulse velocity (V_p), Schmidt hammer rebound number (R_n), effective porosity (n_e), total porosity (n_t), dry density (γ_d), point load index (I_s50), shear wave velocity (V_s), Brazilian tensile strength (BTS), slake durability index (SDI), or by using an artificial neural network (ANN). This paper presents a comparative study for an additional approach to determine the UCS, namely by converting triaxial test results to the UCS. This research has been conducted to encourage further utilisation of laboratory test results obtained from triaxial tests. Triaxial tests are normally performed to determine the intact rock strength envelope. By further utilisation of triaxial test data to estimate the UCS, the database of the UCS can be enriched for rock engineering projects. The method can also be used in cases of limited samples or when samples are too challenged for carrying out a USC test. The research showed that the UCS derived by this method is more direct and more accurate than many empirical methods. Furthermore, the test procedure described herein (carrying out the UCS and triaxial tests on the same sample) can be used to evaluate the accuracy of the calculated UCS. It is important to bear in mind that the scope of this study is not to develop a new method or replace the traditional uniaxial compression test with the method presented in this paper. The purpose and intention is to document that the test results obtained from triaxial testing can be utilised to also provide values of UCS from the same samples. Full article

Reinforced concrete shear walls (RCSWs) are complicated to compute their shear capacity due to their large cross-sectional height-to-thickness ratios and the fact that they are subjected to vertical loads. Numerous factors influence RCSWs’ shear strength capacity, and the analytical models find it challenging to fully account for each factor’s impact on RCSWs’ shear-bearing capacity. Machine learning (ML) technology can deeply capture the mapping relationship between each input feature and the target value, and provide a more flexible and effective prediction method for RCSW shear-bearing capacity. To this end, a shear capacity test database containing 583 RCSW specimens was first established and characterized, and then the database was employed to train single, ensemble, and deep learning models for the shear strength of shear walls and combined with hyper-parameter tuning to enhance each model’s prediction performance, after which the prediction performance of each model was compared. Then, the ML models were contrasted with conventional techniques founded on the mechanical premise. Finally, in order to improve the prediction accuracy and reliability of the ML methods, the individually trained models were integrated into a stacking model using the stacking method, and the stacking model’s prediction performance was assessed. The results of this study show that in the single model, the test set R² of the decision tree (DT) reaches 0.94, showing good trend-capturing ability. Among the ensemble models, Gradient Boosting (GB) performs the best and is comparable to DT in terms of RMSE and R² and significantly outperforms other ensemble methods, such as Random Forest (RF) and Bagging. Deep Neural Networks (DNNs) show the strongest predictive ability among all models, with the lowest RMSE (263 kN) and a test R² of 0.95, which is much better than the majority of ensemble models. The ML models show high accuracy and reliability compared to the traditional RC shear wall shear capacity models. The stacking model has an R² of 0.98 and a CoV of 0.147 in the test set, and it is much better than other independent ML models (R² = 0.88~0.95, CoV = 0.179~0.651). Full article

The assessment methods for estimating the behavior of the complex mechanics of reinforced concrete (RC) structural elements were primarily based on experimental investigation, followed by the collective evaluation of experimental databases from the available literature. There is still a lot of uncertainty in relation to the strength and deformability criteria that have been derived from tests due to the differences in the experimental test setups of the individual research studies that are being fed into the databases used to derive predictive models. This research work focuses on structural elements that exhibit pronounced strength degradation with plastic deformation and brittle failure characteristics. The study’s focus is on evaluating existing models that predict the shear strength of RC columns, which take into account important factors including the structural element’s ductility and axial load, as well as the contributions of specific resistance mechanisms like that of concrete, transverse, and longitudinal reinforcement. Significantly improved predictive models are proposed herein through the implementation of machine learning (ML) algorithms on refined datasets. Three ML models, LREGR, POLYREG-HYT, and XGBoost-HYT-CV, were used to develop different predictive models that were able to compute the shear strength of RC columns. According to the numerical findings, POLYREG-HYT- and XGBoost-HYT-CV-derived models outperformed other ML models in predicting the shear strength of rectangular RC columns with the correlation coefficient having a value R greater than 99% and minimal errors. It was also found that the newly proposed predictive model derived a 2-fold improvement in terms of the correlation coefficient compared to the best available equation in international literature. Full article

Ultra-high-performance concrete (UHPC) is a recently emerged material with exceptional durability and ductility. While widely used in bridge retrofitting, particularly to replace expansion joints and deck overlays, UHPC has seen limited use in jacketing piers for the improvement of lateral load resistance. It presents superior mechanical properties and deformation resilience, enabled by the distributed fibers and the dense microstructure, providing corrosion resistance and a maintenance-free service life. The significant tensile strength and ductility establish UHPC as an attractive resilient jacketing system for structural members. The experimental literature documents the effectiveness of this solution in enhancing the strength and ductility of the retrofitted member, whereas premature modes of failure (i.e., lap splices and shear failure in lightly reinforced piers) are moderated. A comprehensive database of tests on UHPC-jacketed piers under lateral loads was compiled for the development of practical guidelines. Various UHPC jacket configurations were evaluated, and detailed procedures were developed for their implementation in bridge pier retrofitting. These procedures include constitutive models for UHPC, confined concrete, and the strengthening of lap splices, flexure, and shear resistance. The results are supported by the database, providing a solid foundation for the broader application of UHPC in improving the lateral load resistance of bridge piers. Full article

Monitoring the stability of tailings storage facilities (TSFs) is extremely important due to the catastrophic consequences of instability, which pose a threat to both the environment and human life. For this reason, numerous laboratory and field tests are carried out around dams. An extensive database is collected as part of monitoring and field research. The in-depth analysis of available data can help monitor stability and predict disaster hazards. One of the important factors is displacement, including surface displacements—recorded by benchmarks as well as underground displacements—recorded by inclinometers. In this work, methods were developed to help assess the stability of the TSF in terms of surface and underground displacement based on the simulated data from geodetic benchmarks. The context of spatial correlation was investigated using hot spot analysis, which shows areas of greater risk, indicating the places of correlation of large and small displacements. The analysis of displacements versus time allowed us to indicate the growing exponential trend, thanks to which it is possible to forecast the trend of future displacements, as well as their velocity and acceleration, with the coefficient of determination of the trend matching reaching even 0.97. Additionally, the use of a geographically weighted regression model was proposed to predict the risk of shear relative to surface displacements. Full article

The water diversion surge shaft is vital for a hydropower station. However, the complex geological properties of the surrounding rock make it challenging to obtain its mechanical parameters. A method combining particle swarm optimization (PSO) and support vector machine (SVM) algorithms is proposed for estimating these parameters. According to the engineering geological background and support scheme, a three-dimensional model of the water diversion surge shaft is established by FLAC3D. An orthogonal test is designed to verify the accuracy of the numerical model. Then, the surrounding rock mechanical parameter database is established. The PSO-SVM intelligent inversion algorithm is used to invert the optimal values of the mechanical parameters of the surrounding rock. The support for excavating the next layer depends on the mechanical parameters of the current rock layer. An optimized design scheme is then compared and analyzed with the original support scheme by considering deformation and plastic characteristics. The research results demonstrate that the PSO-SVM intelligent inversion algorithm can effectively improve the accuracy and efficiency of the inversion of rock mechanical parameters. Under the influence of excavation, the surrounding rock in the plastic zone mainly fails in shear, with maximum deformation occurring in the middle and lower parts of the excavation area. The maximum deformation of the surrounding rock under support with long anchor cables is 0.6 cm less than that of support without long anchor cables and 4.07 cm less than that of support without an anchor. In the direction of the maximum and minimum principal stress, the maximum depth of the plastic zone under the support with long anchor cables is 1.3 m to 2.6 m less than that of the support without long anchor cables and the support without an anchor. Compared with the support without long anchor cables and support without an anchor, the support with long anchor cables can effectively control the deformation of the surrounding rock and limit the development of the plastic zone. Full article

The use of UHPC as an overlay and repair material in the bridge industry has been increasing recently. Ensuring sufficient interface shear strength between the substrate and UHPC is necessary for adequate performance and the structural integrity of the composite section. Due to the lack of structural design code for UHPC members, designers often rely on experimental data developed by researchers or on existing conventional concrete design code to predict the interface shear capacity of sections containing UHPC. The various test methods currently used to quantify the interface shear strength oftentimes produce different results. The objectives of this paper are to create a database of the studies on the interface shear strength of UHPC members available in the literature and carry out a statistical assessment. Moreover, a reliability analysis is conducted on the collected experimental database, and the probability of failure is determined for UHPC–concrete, UHPC–UHPC, and monolithic UHPC interfaces. The paper also investigates the dependence of the reliability index on two different test methods used for interface shear capacity prediction. Additionally, a simpler interface shear capacity model with readily determined parameters is proposed for the monolithic UHPC interface, with a better reliability index compared to current design specification. Full article

This study demonstrated the potential of a non-destructive evaluation (NDE) method to assess the elastic properties of materials printed under various parameters. A database was created documenting the relationship between the elastic properties (Young’s modulus, shear modulus, and Poisson’s ratio) of PLA (polylactic acid) materials and selected printing parameters such as temperature, speed, and layer height. PLA, which is widely used in additive manufacturing, offers convenient testing conditions due to its less demanding control compared to materials like metals. Ultrasonic testing was conducted on specimens printed under different nozzle temperatures, speeds, and layer heights. The results indicated that an increase in the printing temperature corresponded to an increase in material density and elastic properties of the material. In contrast, an increase in layer height led to a decrease in both density and the elastic properties of the material. Variations in the nozzle speed had a negligible effect on density and did not show a notable effect on the elastic moduli. This study demonstrated that ultrasonic testing is effective in measuring the elastic properties of PLA materials and shows the potential of real-time ultrasonic NDE. Full article

Fiber-reinforced polymers (FRPs) are increasingly being used as a composite material in concrete slabs due to their high strength-to-weight ratio and resistance to corrosion. However, FRP-reinforced concrete slabs, similar to traditional systems, are susceptible to punching shear failure, a critical design concern. Existing empirical models and design provisions for predicting the punching shear strength of FRP-reinforced concrete slabs often exhibit significant bias and dispersion. These errors highlight the need for more reliable predictive models. This study aims to develop gradient-boosted regression tree (GBRT) models to accurately predict the shear strength of FRP-reinforced concrete panels and to address the limitations of existing empirical models. A comprehensive database of 238 sets of experimental results for FRP-reinforced concrete slabs has been compiled from the literature. Different machine learning algorithms were considered, and the performance of GBRT models was evaluated against these algorithms. The dataset was divided into training and testing sets to verify the accuracy of the model. The results indicated that the GBRT model achieved the highest prediction accuracy, with root mean square error (RMSE) of 64.85, mean absolute error (MAE) of 42.89, and coefficient of determination (R²) of 0.955. Comparative analysis with existing experimental models showed that the GBRT model outperformed these traditional approaches. The SHapley Additive exPlanation (SHAP) method was used to interpret the GBRT model, providing insight into the contribution of each input variable to the prediction of punching shear strength. The analysis emphasized the importance of variables such as slab thickness, FRP reinforcement ratio, and critical section perimeter. This study demonstrates the effectiveness of the GBRT model in predicting the punching shear strength of FRP-reinforced concrete slabs with high accuracy. SHAP analysis elucidates key factors that influence model predictions and provides valuable insights for future research and design improvements. Full article