Prediction Of Ultimate Strength Research Articles

Effects of transverse loading on the ultimate strength prediction of stiffened panels under biaxial loads: Potential improvements to the IACS rule formulation

The current International Association of Classification Societies (IACS) rule for designing stiffened panels was found in Wang et al. (2024) to conservatively predict the ultimate strength of panels when transverse loads predominate. This conservatism arises from the overestimation of bending moments acting on stiffeners by directly applying the beam analogy in both longitudinal and transverse directions in the rule formulation when evaluating stiffener yielding.To address this issue, this paper applies the orthotropic plate theory to calculate bending moments experienced by the stiffeners of panels under biaxial loads. Orthotropic properties are derived from equivalent sectional areas and moments of inertia. The predicted bending moments are validated through comparisons with numerical simulations. The effects of boundary conditions and deflection patterns with varying numbers of half-waves are discussed. Based on the analytical results, a modification of the IACS rule formulation is proposed by introducing a correction factor to account for transverse loading effects in the bending moment calculations. The factor is a function of the ratio between longitudinal and transverse loading.The modified IACS rule formulation is verified through numerical simulations of aluminium panels using ABAQUS and comparisons with results from existing literature on steel stiffened panels under various biaxial loading conditions. The modified rule formulation shows improved accuracy in predicting the ultimate strength of stiffened panels, particularly in cases with dominating transverse loads. The modified IACS rule formulation can be useful for more reliable and cost-effective design of ships and offshore structures.

Prediction and reliability analysis of ultimate axial strength for outer circular CFRP-strengthened CFST columns with CTGAN and hybrid MFO-ET model

This study develops a novel hybrid machine learning model to estimate the ultimate axial strength and conduct a reliability analysis for outer circular carbon fiber-reinforced polymer (CFRP)-strengthened concrete-filled steel tube (CFST) columns. The experimental datasets are collected and enriched using the conditional tabular generative adversarial network (CTGAN). The column length, the steel properties (cross-section diameter, thickness, and yield strength), the CFRP properties (thickness, tensile strength, and elastic modulus), and concrete strength are selected as input variables to develop the Extra Trees (ET) model hybridized with Moth-Flame Optimization (MFO) algorithm for the ultimate axial strength estimation. The results reveal that the CTGAN can efficiently capture the actual data distribution of CFRP-strengthened CFST columns and the developed hybrid MFO-ET model can accurately predict the ultimate axial strength with a high accuracy (R2 of 0.985, A10 of 0.867, RMSE of 182.810 kN, and MAE of 124.534 kN) based on the synthetic database. In addition, compared with the best empirical model, the MFO-ET model increases the R2 by (6.78% and 13.48%) and A10 by (108.19% and 122.88%) and reduces the RMSE by (68.19% and 66.24%) and MAE by (71.33% and 68.48%) based on real and synthetic databases, respectively. Notably, a reliability analysis is performed to evaluate the safety of the developed MFO-ET model using Monte Carlo Simulation (MCS). Finally, a web application tool is created to make the developed MFO-ET model easier for users to design practical applications.

Performance degradation evaluation of square steel tubes with local penetration damage

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

Ultimate strength prediction of composite laminates containing straight-sided holes, scarfed holes, and bonded repairs

Scarfed repairs are well suited to load carrying aerospace structures because they can improve the strength of a damaged composite laminate while maintaining a smooth aerodynamic profile. However, the strength prediction of such composite repairs is challenging, with strength depending upon the size and shape of the geometric features causing stress concentrations and the laminate stacking sequence. New semi-analytical and finite element analysis strength models are presented along with experimental verification for laminates containing circular and elliptically shaped straight-sided holes, scarfed holes, and adhesively bonded repairs. The new semi-analytical model for open hole ultimate strength is an extension of the point stress criterion. The model can estimate the characteristic damage length based on material properties, stacking sequence, hole shape, and hole size. Strength predictions for adhesively bonded repairs are also presented. The semi-analytical models are comparable in accuracy to the more computationally expensive continuum damage finite element models for the wide range of panels tested, and both model predictions are in close correlation with experimental results.

A Data-Driven DNN Model to Predict the Ultimate Strength of a Ship’s Bottom Structure

Plates and curved plates are essential components in ship construction. In the design stage, the methods used to evaluate the ultimate strength required to confirm the structural safety of plates include prediction through analytical methods, finite-element analysis (FEA), and empirical formulas. However, with nonlinear buckling, the results of the empirical formula and the FEA differ for small flank angles (1~9). As a result, the prediction of the nonlinear ultimate strength of flank angle (1~9) plates still requires significant computation time and cost. To compensate for this, this study performed an ultimate strength prediction method utilizing a deep neural network together with the 4050 curved plate analysis. In addition, this paper presents the analysis results of the nonlinear finite-element method and the geometric shape and ratio of curved plates as training data. Based on the results of this study, designers can more efficiently design appropriate curved plate members by considering the ultimate strength.

Compressive behavior of sustainable rubberized concrete‐filled steel tube columns having various recycled tire rubber aggregate contents and developing a predictive design model

AbstractAs a sustainable solution, employing recycled tire rubber aggregates in the production of concrete is of considerable interest. However, the applicability of rubberized concretes in construction is restricted due to their relatively low mechanical performance. Using rubberized concrete as a filling material in a steel tube provides a suitable solution for overcoming this issue and for using it effectively as a load‐bearing element. The objective of this study is to examine the impact of the material characteristics of the sustainable rubberized concrete‐filled steel tube (Ru‐CFST) and develop a design model based on ultimate strength prediction for the axially loaded Ru‐CFST stub columns with varying contents of tire rubber aggregate. The model was developed through the gene expression programming (GEP) technique by employing the experimental test results to determine the ultimate compressive strength of Ru‐CFST columns. Based on the principal component analysis, the sectional properties of the load‐bearing element (such as outer diameter, thickness, and length), material strengths (such as rubberized concrete and steel tube strengths), and rubber content were determined as statistically significant parameters affecting the ultimate axial strength of Ru‐CFST stub columns and thus, they were considered in the stage of the model generation. Furthermore, the proposed model's performance was compared to that of available models suggested for traditional CFST columns by commonly employed design codes, namely, ACI, AIJ, AISC, CSA, EC4, and GB. Based on the statistical analysis of the results, it can be concluded that the existing formulations have a relatively acceptable randomness and scatter of observations within the distribution, while the proposed design model gives a more accurate prediction for the ultimate bearing capacity of axially loaded Ru‐CFST columns with the highest R‐squared value of about 0.99 and the comparatively lowest mean absolute percent error of 7.54. It can be stated that the proposed GEP‐based design model will encourage engineers to use such sustainable structural members in their designs and constructions.

Hybrid particle swarm optimization and group method of data handling for the prediction of ultimate strength of concrete-filled steel tube columns

This study presents a hybrid model coupling particle swarm optimization (PSO) with group method of data handling (GMDH) for predicting the ultimate strength of rectangular concrete-filled steel tube (RCFST) columns. A large database of 490 data samples collected from the existing literature was used to construct the model. Compared with the optimal model among the nine existing models, the coefficient of variation (COV), mean absolute percentage error (MAPE) and root relative squared error (RRSE) values of all datasets of the PSO-GMDH model were decreased by 58.38 %, 69.22 % and 64.27 %, respectively; while the coefficient of determination (R2) and a20-index values were increased by 34.32 % and 8.65 %, respectively. The results show that the predicted results of PSO-GMDH model are in good agreement with the experimental results and can accurately predict the ultimate strength of rectangular RCFST columns. In addition, a graphical user interface (GUI) has been developed to facilitate the application of the PSO-GMDH model.

Member capacity of cold-rolled aluminium alloy channel columns - part I: Experimental investigation

Cold-rolled aluminium alloy sections have been recently introduced in Australia as newly developed thin-walled products. To fabricate these sections, the roll-forming process used in cold-formed steel production has been employed, which has proven to be more cost-effective than the conventional extrusion process. This paper describes a series of compression tests to investigate member buckling behaviour and ultimate strengths of cold-rolled aluminium alloy 5052-36 channel columns. Twenty-eight specimens of three commercially available sections (C10030, C25025 and C40030) were tested at the University of Sydney. Two configurations of end boundary conditions were specially designed to capture flexural, flexural-torsional and local-global interaction buckling failure modes. Prior to testing, initial geometric imperfections were measured using a laser scanning method, and flat/corner coupons were tested to determine compressive and tensile strengths of the aluminium alloy material. The experimental results are compared with ultimate column strength predictions by current Australian/New Zealand, North American and European design specifications for aluminium structures. Additionally, the test results provide data for calibration of finite element models of cold-rolled aluminium structural members for use in parametric and similar studies. A companion paper describes such parametric study and the derivation of new design guidelines for member buckling of cold-rolled aluminium channel columns based on the numerical parametric data and the experimental data presented herein.

Multiscale nonlinear analysis of 3D orthogonal woven C/SiC composites based on the CT reconstruction

AbstractDue to the effects of manufacturing process, there are lots of distributed voids in the interior area of three‐dimensional (3D) woven C/SiC composites, leading to the difficulties in predictions of damage evolution and ultimate strength. In this work, the mesoscale geometry of 3D woven C/SiC composites is reconstructed based on the high‐resolution CT scanning, considering the yarn path, yarn profile, and void content. The CT‐based reconstructed model agrees well with the experimental observations. Then, the constitutive laws of mesoscale constituents and macroscale structure are established and implemented numerically by the user‐defined material subroutine UMAT. Finally, the established constitutive laws are applied for the reconstructed model, and the mesoscale and macroscale nonlinear progressive damage behavior of 3D woven C/SiC composite structures are predicted using the hierarchical multiscale method, which are also validated by the experiments. The established CT‐based multiscale computational scheme would be a potential tool for performance evaluation and design of composites.

Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning

AbstractExploring the “composition‐microstructure‐property” relationship is a long‐standing theme in materials science. However, complex interactions make this area of research challenging. Based on the image processing and machine learning techniques, this paper proposes a multimodal fusion learning framework that comprehensively considers both composition and microstructure in prediction of the ultimate tensile strength (UTS) of Al‐Si alloys. Firstly, the composition and image information are collected from the literature and supplementary experiments, followed by the image segmentation and quantitative analysis of eutectic Si images. Subsequently, the quantitative analysis results are combined with other features for three‐step feature screening, and 12 key features are obtained. Finally, four machine‐learning models (i.e., decision tree, random forest, adaptive boosting, and extreme gradient boosting [XGBoost]) are used to predict the UTS of Al‐Si alloys. The results show that the quantitative analysis method proposed in this paper is superior to Image‐Pro Plus (IPP) software in some aspects. The XGBoost model has the best prediction performance with R2 = 0.94. Furthermore, five mixed features and their critical values that significantly affect UTS are identified. Our study provides enlightenment for the prediction of UTS of Al‐Si alloys from composition and microstructure, and would be applicable to other alloys.

Passive stress-strain model and ultimate strength prediction for short steel tube confined concrete (STCC) columns allowing for size effect

Steel tubes can significantly enhance the strength and ductility of concrete-filled steel tube (CFST) and steel tube confined concrete (STCC) columns. Existing research work have mainly focused on the former type while the later type of members not only avoid longitudinal steel bulking but also provide better ductility performance to resist seismic loads which is further investigated in this paper. Based on partial-interaction shear-friction mechanics, a passive stress-strain model for short steel tube confined concrete columns is proposed. The main benefit of the proposed model is that it is based on mechanics for rectangular and circular cross-sections which could allow for the size effect. Besides, this model also gives the ultimate strength of steel tube confined concrete columns and is further simplified as a design-oriented approach. The proposed passive stress-strain model and the simplified model for the ultimate strength of concrete columns show good correlation with experimental data. Compared with three other existing approaches, the simplified model could enhance the computing efficiency to a new level. Then a parametric study is conducted to investigate the effect of specimen size on the behaviour of steel tube confined concrete columns.

Characteristic and allowable compressive strengths of Dendrocalamus Sericeus bamboo culms with/without node using artificial neural networks

The strength of construction material is a crucial consideration in the process of structural design and construction. Conventional materials such as concrete or steel have been widely utilized due to their predictable material performance. However, a significant obstacle to the widespread use of bamboo in structural elements lies in the challenge of its standardization. Many previous research studies have explored bamboo’s load bearing capacity, but the information remains limited due to variations in species, size, age, physical properties, moisture content, and other factors, making it difficult to predict their load-bearing capacity. This study aims to propose Artificial Neural Network (ANN) models to predict ultimate compressive load and compressive strength of Dendrocalamus Sericeus bamboo culm. Additionally, for structural design purposes, the proposed ANN models were employed to determine the characteristic and allowable compressive strengths. As a first step, experimental data from compressive tests in the literature were used for training and developing the ANN model. To investigate the effect of the node on compressive loading capacities, the test data were separated into two datasets, “Node” samples and “Internode” samples. Through the training process, ANN models were finally proposed, and the R-square values for the prediction of ultimate compressive load and compressive strength from the proposed ANN models were significantly higher than those obtained from the linear regression analyses used in the literature. Subsequently, the characteristic and allowable compressive strengths were calculated and compared to the strengths obtained from the experiment data, revealing a difference of approximately only 8.0%. Overall, the ANN models presented in this study offer promising predictive ability for both ultimate compressive load and compressive strength of Dendrocalamus Sericeus bamboo culm, as well as for determining characteristic and allowable strengths. Hence, ANN models are suggested to be adopted as a tool for the design and construction of bamboo buildings.

Adhesion and Sliding Constitutive Relationship between Basalt–Polypropylene Hybrid Fiber-Reinforced Concrete and Steel Bars

The extreme marine environment of the South China Sea Islands, which features high temperatures, high humidity levels and high salt levels, seriously affects the safety of building structures. The durability of concrete can be significantly improved by adding a basalt–polypropylene hybrid fiber, but its bonding mechanism with deformed bars is complicated. Therefore, the bonding performance of hybrid basalt–polypropylene fiber-reinforced concrete and deformed bars was studied by combining experiments and a theoretical analysis. We designed 38 groups of different concrete strengths, different thicknesses of concrete covers, different anchor lengths and different diameters of rebars. The bond strengths, bond–sliding curves and failure forms of each pull specimen were compared and analyzed. The results showed that the failure forms and bond–slip curves of the basalt–polypropylene hybrid fiber-reinforced concrete specimens and the ordinary concrete specimens were essentially the same. Based on the results of the axial tensile tests, an ultimate bond strength prediction model was developed, and a bond–sliding constitutive model for hybrid fiber-reinforced concrete and steel bars was also established.

Prediction of ultimate tensile strength of friction stir welding joint using deep learning-based-multilayer perceptron and long short term memory networks

Friction stir welding (FSW) is a solid-state joining technique where the joint strength is mainly influenced by three process parameters, namely, spindle speed (N), welding speed (V), and plunge force (Fz). The modelling of complex relationships between the process parameters and joint strength requires many experiments, which is a challenging, time-consuming, and non-economical affair. To tackle this problem, computational mathematical models such as deep learning (DL) can be employed to predict the joint strength reliably. In this paper, DL techniques, namely, deep multilayer perceptron (DMLP) and long short-term memory (LSTM) networks have been proposed for such a purpose. The DL networks were first trained with the FSW experimental data and then, the pre-trained models were used for predicting the weld strength. It was found that the DMLP and LSTM models provided lower prediction errors, which are RMSE of 3.30 and 7.63, respectively, and can be effectively utilized for determining weld quality. The proposed DL-based techniques were further compared with the traditional models – the shallow artificial neural network (SANN) model having an RMSE of 27.11 and the ANFIS model having an RMSE of 5.31. DMLP was found to be superior in determining the weld strength most accurately.

Shear resistance and deflection prediction of steel–concrete–steel sandwich panel with headed stud connectors

Bending tests were conducted on eight specimens to investigate the mechanical behavior of steel–concrete-steel (SCS) sandwich panels with headed studs, taking into account the effects including loading mode, steel plate thickness, concrete core thickness and connectors spacing, and one sandwich panel was used as a control specimen. The failure characteristics and crack patterns of the specimens were recorded and the vertical displacement in the span was measured. The test results were used for the validation of the finite element model (FEM). Further parametric studies of the FEM yielded the effect of steel plates on the load-bearing capacity of the sandwich structure. Analytical models applicable to the initial stiffness and shear resistance of specimens with headed studs were proposed. The shear strength considered the effect of steel plate and pin action. The deflection calculation models of the sandwich panel were obtained based on the energy method and were in good agreement with the load–displacement response curves obtained by FEM results. The research results of this paper will have an impact on the large deformation and ultimate strength prediction of sandwich protective panels.

Prediction of ultimate strain and strength of CFRP-wrapped normal and high-strength concrete compressive members using ANN approach

The literature is deficit in predicting the axial strength (AS) and axial strain of carbon fiber reinforced polymer (CFRP)-wrapped normal strength concrete (NSC) and high strength concrete (HSC) compressive members using machine learning techniques. The already proposed models for predicting the AS and axial strain of CFRP-wrapped NSC compressive members were developed using a general regression analysis technique based on a small number of noisy data points by considering the limited parameters of specimens. Therefore, there is a need for a refined and accurate theoretical model for capturing the AS and axial strain of compressive members. The main objective of the current study is to develop the theoretical models for predicting the AS and axial strain of CFRP-wrapped NSC and HSC compressive members using machine learning methods. Two different analysis approaches are employed for securing the objective of the present study. The first approach is using the general regression analysis technique, and the second one is employing artificial neural networks (ANN) modeling. The testing database employed in the analysis consists of the testing results of 364 concrete compressive members subjected to compressive loading. The accuracy of the proposed empirical and ANN models is evaluated and compared on the basis of the testing results. Three statistical indices were employed to determine the performance of the currently proposed models developed in the present study. The ANN models presented the accuracy with the statistical indices R 2 = 0.984, RMSE = 0.112, and MAE = 0.097 for the strength model and R 2 = 0.942, RMSE = 1.211, and MAE = 0.978 for the strain model. The suggested empirical model presented the statistical indices R 2 = 0.90, RMSE = 0.33, and MAE = 2.45 for the strength model and R 2 = 0.80, RMSE = 2.05, and MAE = 5.34 for the strain model. The statistical evaluation showed that the ANN models are more effective and precise than the empirical ones in predicting the AS and axial strain of CFRP-wrapped circular concrete compressive members.

Coupled behaviour and strength prediction of tapered CFDST columns with large hollow ratios for wind turbine towers

Concrete-filled double-skin steel tubular (CFDST) tower has emerged as a promising type of wind turbine support structure with excellent strength and stiffness. As the basic member in CFDST towers, tapered concrete-filled double-skin steel tubular columns with large hollow ratios (TLHR-CFDSTs) are frequently subjected to complex combined compression-bending-torsion loads. This paper presents a comprehensive analytical study on the coupled behaviour of the TLHR-CFDST column under combined loads and the relevant ultimate strength prediction method. A FE model was established to conduct numerical analyses and extensive parameter studies while the data from the previous experimental research was used to provide a verification basis. It is found that the column subjected to combined loads has three typical failure patterns, which are mainly governed by the bending-to-torsion ratio (mt). Columns with moderate mt undergo the bending-torsion failure and show obvious coupled performances. The load-deformation relation, the ultimate characteristics and the effects of important parameters, including axial compressive ratio (n), tapered angle (θ), hollow ratio (χ) and height-to-diameter ratio (λ) were discussed. The ultimate strength correlations of the column were revealed by analysing strength data within the parameter scopes commonly used in wind turbine towers. Lastly, a practical and accurate prediction method for the ultimate strength of the TLHR-CFDST column under combined compression-bending-torsion loads was proposed.

Prediction of ultimate strength of FRP-confined predamaged concrete using backward multiple regression motivated soft computing methods

Prediction of ultimate strength of FRP-confined predamaged concrete using backward multiple regression motivated soft computing methods

Simplified criterion to predict the lower bounds of the anisotropic ultimate strengths of 3D printing metals

3D printing metals have an intrinsic building orientation, so they have some anisotropy in their strength due to the failure between the printing layers with direction dependence that was commonly seen in composite materials. Although the Tsai-Hill composite strength criterion predicted the ultimate strengths of some 3D printing metals made with selective laser melting and laser powder bed fusion (SLM/LPBF), the current study showed that the composite strength criterion was not applicable to the ultimate strength predictions of all 3D printing metals. Our suggested quadratic strength criterion without stress interaction provided conservative strength predictions to ensure safe 3D printing metal applications. Moreover, the shear strength plays a critical role in strength predictions and should be measured accurately.

Uncertainty Analysis of Ultimate Strength for Spherical Shells Subjected to External Pressure

To evaluate the stochastic characteristics of ultimate strength for spherical shells subjected to external pressure, uncertainty analysis is conducted in this study. Experimental measurements and result analysis of dimensions, as well as ultimate strength, are performed. The basic theories of the surrogate model and probability-box method for ultimate strength are introduced briefly. Uncertainty analysis of ultimate strength is completed utilizing double-nested random sampling based on the established Gaussian process model with high precision, and the results are compared with the experimental findings. The results show that the experimental empirical cumulative distribution function is contained in the probability-box obtained while considering the influence of welding, and the absolute errors of the mean value, as well as those of the standard deviation, are much smaller. The study verified that the influence of welding cannot be ignored, and the prediction of ultimate strength, considering uncertainties, can eliminate the occasionalities of simulations and experimental tests.