Prediction Of Bearing Capacity Research Articles

Pull-out behavior and failure mechanism of steel expansion anchors in high-performance steel fiber reinforced concrete(HPSFRC)

Past research on the pull-out performance of anchors mainly focuses on normal concrete(NC) concretes. New design methods are needed for the pull-out performance of anchors in the concretes of new cementitious materials with high strength and toughness. Accordingly, the pull-out behavior of torque-controlled expansion anchor(TCE) in high-performance steel fiber reinforced concrete(HPSFRC) concrete with different fiber content and installation conditions(embedment depth and anchor diameter) was investigated. The focus was on the failure modes, crack morphology, and load-displacement behavior(including pull-out load-bearing capacity, anchorage stiffness, and anchorage toughness). The addition of fibers leads to increased load-bearing capacity and toughness, reduced stiffness, and all failure modes exhibited ductile cracking and sufficient tensile ductility. Both diameter and depth had a positive impact on load-bearing capacity. A broader database based on literature and this study was analyzed for further exploration of the effects of fibers. The accuracy of predicting load-bearing capacity was compared between two empirical models(concrete capacity design-CCD and Toth models) and the proposed modified model. The CCD model greatly underestimates capacity, and the Toth model is unsuitable for high fiber content(exceeds 2.0 %) HPSFRC scenarios. The modified model showed the best bearing capacity prediction for anchors on HPSFRC concretes, with a mean ratio of predicted values to experimental values reaching 1.01–1.08 and a coefficient of dispersion below 15.0 %.

Prediction of bearing capacity of geogrid-reinforced sand over stone columns in soft clay

It is a challenge to estimate the ultimate bearing capacity (qrs) of a geogrid-reinforced sandy bed on vertical stone columns in soft clay due to their complex geometry and uncertain parameters. Predicting qrs can be costly and challenging, so creating an accurate prediction model is important for real-world applications. The research aims to use ensemble techniques like K Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) to develop a model for estimating the bearing capacity of geogrid-reinforced stone columns (GRBS) on a sandy bed with vertical stone columns in soft clay. A dataset of 245 experimental observations is used to train the models. The present study highlights the potential use of the XGBoost model as a useful tool that can assist in predicting the bearing capacity. The results of the study reveal that this model has performed well in predicting the bearing capacity with high correlation coefficients of 0.9947. Furthermore; a SHAP dependency analysis was conducted to ascertain the significance of each parameter.

Toward Smart Urban Management: Integrating Geographic Information Systems and Geology for Underground Bearing Capacity Prediction in Casablanca City, Morocco

To effectively manage the sustainable urban development of cities, it is crucial to quickly understand the geological and geotechnical attributes of the underground. Carrying out such studies entails significant investments and focused reconnaissance efforts, which might not align seamlessly with large-scale territorial planning initiatives within a city accommodating more than 3 million inhabitants, like Casablanca in Morocco. Additionally, various specific investigations have been conducted by municipal authorities in recent times. The primary aim of this study is to furnish city managers and planners with a tool for informed decision-making, enabling them to explore the geological and geotechnical properties of soil foundations using Geographic Information Systems (GISs) and geostatistics. This database, initially intended for utilization by developers and construction engineers, stands to economize a substantial amount of time and resources. During the urban planning of cities and prior to determining land usage (five- or seven-floor structures), comprehending the mechanical traits (bearing capacity, water levels, etc.) of the soil is crucial. To this end, geological and geotechnical maps, along with a collection of 100 surveys, were gathered and incorporated into a GIS system. These diverse data sources converged to reveal that the underlying composition of the surveyed area comprises silts, calcarenites, marls, graywackes, and siltstones. These formations are attributed to the Middle Cambrian and the Holocene epochs. The resultant geotechnical findings were integrated into the GIS and subjected to interpolation using ordinary kriging. This procedure yielded two distinct maps: one illustrating bearing capacity and the other depicting the substratum. The bearing capacity of the soil in the study zone is rated as moderate, fluctuating between two and four bars. The depth of the foundation remains relatively shallow, ranging from 0.8 m to 4.5 m. The outcomes are highly promising, affirming that the soil in Casablanca boasts commendable geotechnical attributes capable of enduring substantial loads and stresses. Consequently, redirecting future urban planning in the region toward vertical expansion seems judicious, safeguarding Casablanca’s remaining green spaces and the small agricultural belt. The results of this work help to better plan the urban development of the city of Casablanca in a smarter way, thus preserving space, agriculture, and the environment while promoting sustainability. In addition, the databases and maps created through this paper aim for a balanced financial management of city expenditures in urban planning.

Study on Compression Bearing Capacity of Tapered Concrete-Filled Double-Skin Steel Tubular Members Based on Heuristic-Algorithm-Optimized Backpropagation Neural Network Model

A tapered concrete-filled double-skin steel tubular (TCFDST) structure has been used as the main framework in transmission towers, offshore facility platforms, and turbine towers owing to its excellent mechanical properties. In order to solve the difficulties of calculating the axial compressive capacity of TCFDST members due to the variations in cross-section, this paper applied heuristic optimization algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO) to enhance a Backpropagation Neural Network (BPNN) model. A predictive model incorporating both global and local optimization strategies for the axial compressive capacity of a TCFDST structure is proposed. A comprehensive axial database for TCFDST members, comprising 1327 sets of experimental and finite element analysis results, was established, with ten types of component dimensions and material parameters selected as input variables and compressive bearing capacity as the output variable. This study developed and assessed four BPNN models, each optimized by a different heuristic algorithm, against various machine learning algorithms and standards. The heuristic-algorithm-optimized BPNN models demonstrated superior accuracy in predicting the axial compressive capacity of TCFDST members. Through parametric analysis, this study identified the relationship between the model’s bearing capacity predictions and each input parameter, confirming the model’s broad applicability. The optimized BPNN model, refined with heuristic algorithms, provides a significant reference for addressing the computational challenges associated with the load-bearing capacity of TCFDST structures and facilitating their application.

A novel hybrid model for predicting the bearing capacity of piles

Due to the uncertainty of soil condition and pile design characteristics, it is always a challenge for geotechnical engineers to accurately determine the bearing capacity of piles. The main objective of this study is to propose a hybrid model coupling least squares support vector machine (LSSVM) with an improved particle swarm optimization (IPSO) algorithm for the prediction of bearing capacity of piles. The improved PSO algorithm was used to optimize the LSSVM hyperparameters. The performance of the IPSO-LSSVM model was compared with seven artificial intelligence models, namely adaptive neuro-fuzzy inference system (ANFIS), M5 model tree (M5MT), multivariate adaptive regression splines (MARS), gene expression programming (GEP), random forest (RF), regression tree (RT) and a stacked ensemble model. Six statistical indices (e.g., coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), relative root mean squared error (RRMSE), BIAS and discrepancy ratio (DR)) were used to evaluate the performance of the models. The R2, MAE, RMSE, RRMSE and BIAS values of the IPSO-LSSVM model were 1, 4.27 kN, 6.164 kN, 0.005 and 0, respectively, for the training datasets and 0.9977, 22 kN, 36.03 kN, 0.0275 and –11, respectively, for the testing datasets. Compared with the ANFIS, MARS, GEP, M5MT, RF, RT and the stacked ensemble models, the proposed IPSO-LSSVM model shows high accuracy and robustness on the test datasets. In addition, the sensitivity, uncertainty, reliability and resilience of the IPSO-LSSVM model were also analyzed in this study. First published online 22 October 2024

Eccentric compression behaviors of iron tailings and recycled aggregate concrete-filled steel tube columns

This paper presents a sustainable method for using iron ore tailings (IOTs) sand and recycled coarse aggregate (RCA) in concrete and concrete-filled steel tube (CFT) columns. The study first examines the impact of RCA replacement ratios (0 %, 50 %, 100 %) on concrete's mechanical properties, finding that both compressive and tensile strengths decrease with higher RCA content due to the negative effects of old interfacial transition zones and adhered cement in RCA. Then, twelve iron tailing and recycled aggregate concrete-filled steel tube (ITRAC-CFT) columns were tested under eccentric compression to assess the effects of varying RCA replacement ratios, eccentricity ratios (0.3, 0.5), and slenderness ratios (12.12, 19.05). Results indicate that RCA ratios have minimal impact on failure modes, ultimate bearing capacity, and stiffness, although ductility increases with higher RCA content. As slenderness ratios increase, the ultimate bearing capacity decreases by 5.3 % and 6.8 % for eccentricities of 0.3 and 0.5, respectively. The study further validates these findings through the finite element method (FEM), showing strong agreement between experimental and predicted results, average value, standard deviation, and coefficient of variation of Nu/NFE were 1.02 %, 0.04, and 3.92 %, respectively. Finally, a comparison with existing design codes (EC4, AISC 360, and GB 50396) reveals that AISC 360 provides the most accurate predictions of the ultimate bearing capacity of ITRAC-CFT columns. The research concludes that the comprehensive use of IOTs sand and RCA in CFT structural concrete is a highly viable and eco-friendly solution, offering potential benefits for sustainable construction practices.

Axial compression performances and bearing capacity prediction of self-compacting fly ash concrete filled circle steel tube columns

To solve the problem of a large amount of fly ash accumulation and study the axial compression and bearing capacity prediction of the self-compacting fly ash concrete filled circle steel tube (SCCFST) columns, eight specimens are designed to explore the impact of concrete strength grade, internal structural measures, and additional parameters. The stress, progression of deformation, and failure mode of each specimen are observed during the loading process. The load–displacement curves, load-strain curves, characteristic load and displacement, ductility, and stiffness degradation are analyzed. The findings revealed that shear deformation occurred predominantly in the middle and upper portions of the steel tubes. Enhancing the strength of the concrete or adopting internal structural measures could increase the bearing capacity and ductility of the specimens. The peak load and ductility could be increased by up to 17.6 and 53.6%, respectively. The proposed unified calculation equation for the axial compression bearing capacity of SCCFST columns demonstrates notable reliability and precision. Furthermore, these tests offer valuable references for the engineering application of various forms of SCCFST columns, which are of significant importance in practical engineering.

Prediction of ultimate bearing capacity of RC beams after fire based on data-driven method

Prediction of ultimate bearing capacity of RC beams after fire based on data-driven method

Pull-out strength of thin perfobond connectors and C-ties in steel-concrete-steel sandwich composite structures

Due to the remarkable mechanical property, short construction period, and labor-saving features, steel-concrete-steel (SCS) sandwich composite structures are widely used in assembled buildings, modularized immersed tunnels, and bridge cable pylons. In SCS sandwich structures, sufficient pull-out resistance in connectors is necessary to effectively prevent outward buckling of the outer steel faceplates. However, the existing connectors remain deficient in terms of load bearing capacity, ductility, and construction convenience. A novel type of connector named thin perfobond connector and C-tie (PBL-CT) was proposed to address these problems in this paper. While retaining the superior shear performance of traditional perfobond connectors (PBLs), the addition of C-ties in PBL-CTs enhances structural shear resistance. Through 18 monotonic pull-out tests, the pull-out performance of shallow-embedded thin PBL-CTs was investigated. Test results revealed that the primary failure mode of shallow-embedded thin PBLs was cracking of the surrounding concrete, with C-ties significantly improving the pull-out performance and shifting the failure mode to a combination of concrete breakout and perforated rib rupture. A nonlinear finite element (FE) model was developed and calibrated to conduct parameter analyses on concrete strength, embedment depth, and perforated rib parameters. This led to the establishment of a mixed failure model for shallow-embedded thin PBL-CTs. The ultimate pull-out bearing capacity was found to comprise two components: the pull-out resistance of the concrete cone and the shear resistance of the perforated rib. A regression-based prediction formula was derived, providing accurate and conservative predictions of the ultimate pull-out bearing capacity of shallow-embedded thin PBL-CTs.

Research on pile bearing capacity prediction improved by grey wolf optimization in the SSA-LSSVM model

Abstract Pile foundations in soft soil often encounter challenges regarding abnormal bearing capacity, significantly impacting the safety of engineering projects. The bearing capacity of pile foundations is influenced by various factors, often characterized by complexity and unpredictability. Therefore, this study proposes a comprehensive model, termed GWOASSA-LSSVM, based on Grey Wolf Optimization (GWO)-enhanced Sparrow Search Algorithm (SSA) and Least Squares Support Vector Model (LSSVM). By introducing a grey wolf hierarchy to enhance the global search capability of the sparrow search algorithm and automating the optimization of parameters (γ, δ) in the LSSVM model using GWO, the GWOASSA-LSSVM predictive model is established. Evaluation metrics including correlation, and correlation between pile bearing capacity and the predictive target are considered, with pile bearing capacity as the predictive target. The GWOASSA-LSSVM model is compared with SSA-LSSVM, LSSVM, and Back Propagation Neural Network (BPNN). Results indicate that the GWOASSA-LSSVM model outperforms SSA-LSSVM, LSSVM, and BPNN across metrics such as Coefficient of Determination (R2), Variance Accounted For (VAF), Performance Index (PI), Index of Agreement (IOA), Index of Scatter (IOS), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and a20-index, demonstrating its capability for accurate prediction of pile bearing capacity.

Enhancing FRP-concrete interface bearing capacity prediction with explainable machine learning: A feature engineering approach and SHAP analysis

This study introduces a novel approach to predict the shear bearing capacity of FRP-concrete interfaces using explainable machine learning. Eight algorithms are employed: three standalone models (Artificial Neural Network, Support Vector Regression, and Decision Tree) and five ensemble learning models (Bagging, Random Forest, Adaptive Boosting, Gradient Boosting, and Extreme Gradient Boosting). Four scenarios with varying input features, including engineered features inspired by mechanics-based bearing capacity equations, are examined. Notably, the inclusion of engineered features such as the stiffness of the FRP strip (Kf) significantly enhanced prediction accuracy and efficiency, although the width correction coefficient (bf/bc) did not yield significant benefits, contrary to findings in mechanics-based models. The Extreme Gradient Boosting (XGBoost) algorithm emerged as the top-performing model, achieving an impressive R-square value of 0.949. In terms of explainability, the study utilized the Shapley Additive Explanation (SHAP) technique to comprehensively elucidate the significance, dependency, and interaction effects of features within the best-performing model. Remarkable, approximately 75 % of the total mean absolute SHAP values were attributed to Kf and bf of the FRP, highlighting their pivotal role in the prediction process. This detailed explanation of the model using SHAP instills trust in the predictions and facilitates its practical implementation in real-world scenarios.

Machine-learning assisted analysis on the seismic performance of steel reinforced concrete composite columns

This paper presents the data-driven analysis on the seismic performance of steel reinforced concrete (SRC) composite columns by using machine learning (ML) algorithms. A total of 248 test data of SRC columns subjected to the combined axial compressive force and low reversed cyclic horizontal force was collected from the published literatures to form the database. Since the seismic action triggers the SRC columns to suffer from different failure mode and hence exhibit quite different load bearing mechanism, six ML algorithms were employed to facilitate the failure mode classification and bearing capacity prediction. It was found that the Random Forest (RF) model predicts the failure mode most accurately, while XGBoost model delivers the best estimation of bearing capacity. To further evaluate the feasibility of ML models and unveil the interaction between different variables, interpretability analysis of ML models was carried out. In comparison with the conventional empirical judgement and theoretical derivation, ML model delivers enhanced accuracy and robustness to predict the seismic performance of SRC columns, and hence it could be used as a promising alternative for the preliminary design and analysis of SRC columns.

Using meta-heuristic algorithms with multi-layer perceptron for prediction of ultimate bearing capacity

ABSTRACT This study presents an innovative approach to predicting the Ultimate Bearing Capacity (Qu) in civil engineering, integrating a Multi-layer Perceptron (MLP) neural network using the Artificial Rabbits Optimization (ARO) and Improved Manta-Ray Foraging Optimizer (IMRFO) methods. The MLP effectively captures complex, non-linear relationships within geotechnical datasets, while the IMRFO and ARO algorithms fine-tune the MLP’s parameters for optimal performance. Validation results demonstrate the efficacy of this method, with the MLP+IMRFO (MLIM) model achieving an R2 value of 0.996 and a MARE of 0.113, highlighting its potential for enhancing predictive accuracy in civil engineering applications. This combined methodology offers a state-of-the-art solution for Qu forecasting, contributing to safer and more efficient construction practices.

Optimized CatBoost-Based Soft-Computing Models for Prediction of the Ultimate Bearing Capacity of T-Shaped Footings Subjected to Eccentric Load

Optimized CatBoost-Based Soft-Computing Models for Prediction of the Ultimate Bearing Capacity of T-Shaped Footings Subjected to Eccentric Load

Axial compressive mechanical behavior of a CFRP-confined composite member composed of bamboo strips and a thin-walled steel tube

To utilize fully the excellent mechanical properties of bamboo, thin-walled steel, and carbon fiber-reinforced polymer (CFRP), a CFRP-confined composite member composed of bamboo strips and a thin-walled steel tube (CBSC) was proposed to design a bamboo member with high-bearing capacity and ductility. The axial compressive tests were conducted on 26 CBSC members. Based on the validated finite-element (FE) model, a parametric analysis was performed to investigate the effects of different parameters on the axial compressive mechanical behaviors of CBSC members. Finally, the predictions of the ultimate axial compressive bearing capacity derived from the unified theory of concrete-filled steel tube columns agreed well with the test and FE results. The results showed that the failure mode of short CBSC members was strength failure, including material damage in the middle of the member, and end-crushing failure. However, the slender CBSC members exhibited typical global buckling failure. CBSC members can fully utilize the mechanical characteristics of the three materials to exert their combined effects. In terms of improving the bearing capacity, the recommended number of CFRP layers for all CBSC members is one except for the short CFRP-confined bamboo strip, thin-walled, circular steel-tube composite members, which is equal to two. The critical slenderness ratios of the CBCSC specimens and CBSSC specimens for distinguishing strength failure and global buckling are 19 and 36, respectively. The proposed predicted equations can accurately estimate the ultimate bearing capacity of CBSC members under axial compression.

Intelligent Prediction of Stability Bearing Capacity of New-Type Modular Assembled Latticed Shells with Flange Connections

Intelligent Prediction of Stability Bearing Capacity of New-Type Modular Assembled Latticed Shells with Flange Connections

Failure behavior and bearing capacity prediction of steel wire-ring nets under rockfall impact using a coordination model-based analysis method

Flexible barriers have been widely used to protect against rockfall disasters. Steel wire-ring nets, which are key components of the barrier system and directly bear impacts, present several unresolved issues, including the prediction of tensile-flexural stiffness and energy absorption considering ring-scale mechanical behaviors, as well as failure behavior under rockfall impact. In this paper, a coordination model for the tensile-flexural stiffness and elastic–plastic energy distribution of steel wire-ring nets is established, and the model parameters, including failure criteria, are calibrated through tests. The failure mechanism and resistance capacity evolution of net rings considering the influence of contact radius is revealed. The full-scale impact tests of nets were conducted to verify the accuracy of the model to predict net failure. The study demonstrates that the coordination model incorporating nonlinear factors in ring-scale can accurately reproduce the stiffness development and energy absorption of the net-ring, laying a foundation for predicting the failure of nets. The predicted net failure energy has an error within 5% of the results from destructive impact tests. The shape and impact attitude of blocks significantly influence the net failure energy, indicating potential safety hazards in net design posed by the assumption of a 26-face polyhedron impact block in current standards.

Predicting the load-bearing capacity of lipped channel section cold formed steel profiles under combined effects using machine learning

The application areas of cold formed steel (CFS) profiles are progressively expanding. Due to the thin cross-sections of these profiles, buckling problem occurs. Experimental and numerical studies to investigate this behavior are very difficult and time consuming. Therefore, this study aims to predict the load bearing capacity of CFS profiles with different slenderness under combined effects using advanced machine learning (ML) models. In this scope, a detailed finite element model including material nonlinearity and geometrical imperfections was created and 2240 finite element analyses (FEAs) were performed using the model validated by experimental studies. A comprehensive data set consisting of 2240 data points and 9 different features was created from the FEAs. Then, five ML models such as support vector regressor (SVR), random forest (RF), multilayer perceptron (MLP), gradient boosting regressor (GBR) and extreme gradient boosting (XGB) were applied to the dataset to predict the load bearing capacity of CFS profiles. In order to obtain effective results during the implementation phase, cross-validation and hyperparameter fine-tuning were performed to obtain optimum parameters. At the conclusion of the study, all five ML models were considered to be highly effective in predicting the load bearing capacity of CFS profiles. MLP model made better predictions compared to the other four models. In the load bearing capacity prediction, the accuracy of the MLP model is ± 4.26 kN, which is less than 4 % of the average load bearing capacity. It was also found that flange length and section thickness have more effect on the load bearing capacity compared to other properties.

Prediction of bearing capacity of pile foundation using deep learning approaches

Prediction of bearing capacity of pile foundation using deep learning approaches

Prediction of Ultimate Bearing Capacity of Soil–Cement Mixed Pile Composite Foundation Using SA-IRMO-BPNN Model

The prediction of the ultimate bearing capacity (UBC) of composite foundations represents a critical application of test monitoring data within the field of intelligent geotechnical engineering. This paper introduces an effective combinational prediction algorithm, namely SA-IRMO-BP. By integrating the Improved Radial Movement Optimization (IRMO) algorithm with the simulated annealing (SA) algorithm, we develop a meta-heuristic optimization algorithm (SA-IRMO) to optimize the built-in weights and thresholds of backpropagation neural networks (BPNN). Leveraging this integrated prediction algorithm, we forecast the UBC of soil–cement mixed (SCM) pile composite foundations, yielding the following performance metrics: RMSE = 3.4626, MAE = 2.2712, R = 0.9978, VAF = 99.4339. These metrics substantiate the superior predictive performance of the proposed model. Furthermore, we utilize two distinct datasets to validate the generalizability of the prediction model presented herein, which carries significant implications for the safety and stability of civil engineering projects.