Prediction Of Ultimate Strength Research Articles

Unified compressive strength model for axially loaded circular composite short columns

Composite columns are widely used in modern structures owing primarily to their excellent structural performance, but they usually have very different ultimate axial compressive strength prediction models, which may lead to confusion for researchers and engineering designers. In addition, given the similarity of their mechanical mechanisms, there may be a generic prediction model for these columns. Hence, the authors aim to provide a simple and effective unified ultimate compression prediction model for axially loaded circular composite short columns. A rich experimental database is firstly established. Then the force analysis of a typical concrete-filled double-skin steel tubes (CFDST) column is illustrated, and it can be easily extended to other types of composite columns. Based on the above force analysis, the stress state of each component of the column can be determined via different sections and loading concepts. Contributions of the inner steel tube to the load-bearing capacity are further clarified, and the passively confined compressive strength of the concrete core is also investigated on the basis of the proposed equivalent confining stress. Subsequently, the calculation flow of the ultimate compression strength is briefly demonstrated. Finally, the ultimate compression prediction model is evaluated by the established test database, and good consistency can be observed for circular composite short columns with a widely varied parameter.

An integrated ANN and design of experiments technique to optimize the FSW input parameters of novel interlock lap weld

Friction stir welding of lightweight aluminium alloys have advantage in automobile industry with its vast applications. This research work focuses on the influence of FSW process parameters on novel interlock lap weld of AA7075-T7-AA7475 tailor welded blank. Three levels of the parameters, including tool rotation speed (TRS), weld speed (WS) and plunge speed, were used to form L27 orthogonal array to optimize the input process conditions. Ultimate tensile strength and Vicker's micro hardness were measured to test the characteristics of the interlock welded samples. Scanning electron microscopy analyses have been carried out to study the surface morphologies and elemental components in the welded samples. Artificial neural network (ANN) has been used to predict the optimized process parameter associated with the novel interlock lap weld. The TRS and WS contributed significantly in improving the mechanical behaviour and microstructural characteristics of interlock lap welds. Visual inspection and surface morphology analysis showed uniform dispersal of aluminium alloy deposition throughout the interlock weld samples. The ultimate tensile strength and micro hardness prediction was carried out using ANN with 95% accuracy level. The predicted results of ANN were more accurate than the experimental results and regression model of fractional factorial design. The defined FSW interlock lap weld stands out as the substitute for typical FSW lap weld of aluminium alloys which fulfils the modern automotive industry demands in welding monocock frames.

Compressive behavior of stiffened steel tubes for wind turbine towers

Steel tubes adopted in wind turbine towers generally sustain the combination of compression, bending, and torsion loads. The study of compressive behavior is crucial for investigating the combined bearing capacity of the tower and proposing the corresponding design method. Local buckling tends to affect compressive behavior due to the large diameter-to-thickness ratios of the tubes. Thus, the authors proposed a new form of tower based on longitudinally stiffened steel tubes, which were experimentally and analytically studied in this paper. A total of six tube specimens (four stiffened ones and two unstiffened ones) were tested under axial compression. The experimental results indicate that the local buckling of the tube can be substantially prevented by stiffeners and the T-type stiffener exhibits a superior effect. Finite element models were built and validated by the test results, and the parametric analysis was conducted, indicating that the diameter-to-thickness ratio of the steel tube, the yield strength of steel, and the proportion of stiffener area are the prime sensitive parameters affecting the ultimate strength. Ultimate compressive strength prediction methods in design specifications were evaluated. A practical method for calculating the ultimate strength of stiffened steel tubes was subsequently developed in this paper.

Ultimate strength prediction of cracked panels under extreme cyclic loads considering crack propagation

The objective of this study is to gain insights on the failure mechanism and behavior of cracked panels under cyclic loading. Numerical analyses were performed to investigate the ultimate strength of cracked panels. The prefabricated crack is assumed to be transverse and through-thickness, the relevant three types of crack location models are studied. Crack propagation arisen out of extreme cyclic loads is taken into consideration and the element erosion approach is adopted to simulate the process of crack growth. The influences of the amplitude of cyclic loads, the crack location, the panel thickness and the aspect ratio of panel on the ultimate strength of cracked panels under extreme cyclic loads are discussed in detail. Based on numerical results, the prediction formulae for dimensionless ultimate strength considering crack propagation are derived to estimate the ultimate strength of cracked panels under cyclic loads.

Behaviour of composite beam-column joint with fin plate connection subjected to impact loads

Falling-debris impact on a remaining building is detrimental and may lead to propagation of initial damage by abnormal loadings (gas, explosion, etc.), if the structure is not able to resist the impact. This in turn will lead to progressive collapse of the whole structure. For a frame structure, fin plate (or shear tab) connections ideal for gravity-dominant design is normally weaker than seismic connection. Therefore, its integrity under dynamic loading is of great importance for anti-progressive collapse performance. A series of tests on composite joints with fin plate connections subjected to impact loads was conducted to investigate the dynamic behaviour, which served as an unfavourable loading scenario for falling-debris impact which occurred close to the beam-to-column connection. Three middle joints and one side joint were designed and tested in the experimental programme. The test parameters included impact load, joint type and composite slab thickness. Structural response, failure mode and development of strains were investigated. A comparison of test results and design predictions of ultimate strength was conducted. The comparison was also extended to composite joints subjected to quasi-static loads. It was found that an intermediate level of strain rate in the order of 1 s−1 was recorded for the impact tests and it would lead to maximum increases of 28 % in concrete strength and 16 % in steel reinforcement strength, respectively. Compared to quasi-static tests, strain rate effect in the impact tests could increase compressive arch, catenary and flexural resistance of composite FP joints. Moreover, composite slab effect was beneficial to flexural resistance and detrimental to tying resistance, since deformation capacity of fin plate connection was consumed for flexural resistance at small deformation stage.

Application of Equivalent Single Layer Approach for Ultimate Strength Analyses of Ship Hull Girder

The objective of this paper is to present the application of equivalent single layer (ESL) approach for the ultimate strength assessment of ship hull girder in the context of numerical finite element (FE) simulations. In the ESL approach, the stiffened panel is replaced with a single plate, which has the equivalent stiffness of the original panel. Removal of tertiary stiffening elements from the numerical model facilitates time-savings in pre-processing and FE analysis stage. The applicability of ESL approach is demonstrated with two case studies, one compartment model and full-sized double hull tanker model in intact and damaged conditions. The damage extents are determined based on the international association of classification societies from common structural rules (IACS-CSR) for oil tanker. Ship hull girder is exposed to distributed pressure with the sinusoidal shape that bends the hull girder. This pressure load is applied separately to bottom and side structures to obtain the vertical and horizontal bending moments of the hull girder, respectively. Ultimate strength predictions obtained from ESL approach are compared to full three-dimensional finite element method (3D FEM) and IACS incremental-iterative method. The comparison between different methods is provided in terms of longitudinal bending moment and cross sectional stress distribution. Overall, ESL approach yields good agreement compared to the 3D FEM results in predicting the ultimate strength of ship hull girder while providing up to 3 times computational efficiency and ease of modeling.

Out‐of‐plane Stability Design by GMNIA – Equivalent Imperfections and Strain Limits

AbstractAn accurate and consistent approach to the out‐of‐plane stability design of steel beams and structures utilising geometrically and materially nonlinear analysis with imperfections (GMNIA) and strain limits is proposed. The method is implemented using computationally efficient beam elements, with the ultimate structural resistance defined either by (i) the ultimate load factor or (ii) the load factor at which a strain limit, determined on the basis of the continuous strength method (CSM), is attained, whichever occurs first. In the present paper, the scope of this method is extended from the in‐plane design of steel structures and structural components to scenarios in which out‐of‐plane stability effects, with a focus on lateral‐torsional buckling (LTB), govern. First, two shapes and corresponding amplitudes of equivalent imperfections, which consider the combined influence of both geometric imperfections and residual stresses, for the out‐of‐stability design of steel and stainless steel members by GMNIA are established. The existing CSM strain limits for in‐plane design are then extended to allow for the influence of shear, torsion and warping, to enable their general applicability to three‐dimensional buckling problems. It is shown that employing the proposed design method together with the proposed equivalent imperfections generally provides more accurate and consistent ultimate strength predictions than traditional Eurocode design calculations and also streamlines the design process by eliminating the need for cross‐section classification and individual member design checks.

A machine-learning model to predict tensile properties of Ti6Al4V parts prepared by laser powder bed fusion with hot isostatic pressing

In this work, a machine-learning model was established to predict the influence of post-treatment, hot isostatic pressing (HIP) parameters on the tensile properties of Ti6Al4V parts prepared by Laser Powder Bed Fusion (LPBF), also known as Selective Laser Melting (SLM). The database was established by collecting published reports on HIP treatment of LPBF Ti6Al4V from 2010 to 2019, employing the following online resources: Google Scholar, Science Direct, Research Gate, and Springer. Using the established model, it is possible to prescribe HIP parameters and predict properties after HIP for LPBF Ti6Al4V parts with high confidence. The model shows great accuracy in the prediction of Yield Strength (YS) and Ultimate Tensile Strength (UTS). Of the predictions, 87.5% were within a 5% error range for YS in all datasets, and this value was 100% for UTS. It was found that the YS and UTS are sensitive to the HIP parameters, including temperature and holding time. The initial YS and UTS of as-printed parts are weaker influencing factors for YS and UTS of final parts, as compared with the HIP parameters. The model suggests that a HIP process with a holding temperature lower than 970 °C and a holding time of approximately 2 h is sufficient for LPBFed Ti6Al4V parts to achieve mechanical properties that comply with the ASTM standard of Ti6Al4V for surgical implant applications (Standard Specification for Wrought Titanium-6Aluminum-4Vanadium ELI (Extra Low Interstitial) Alloy for Surgical Implant Applications -UNS R56401-). However, the prediction of strain to failure showed lower accuracy. Only 62.1% of predicted strain results were within a 10% error range. The differences among the printing processes by multiple research groups may have resulted in large variations of structural defects and thus led to significant scattering of the elongation data. The model also shows that elongation after HIP has a higher dependence on the as-printed samples.

Prediction and uncertainty quantification of ultimate bond strength between UHPC and reinforcing steel bar using a hybrid machine learning approach

The composite action of the reinforcing bars in the UHPC involves complex and nonlinear mechanisms. Inadequate knowledge of their interaction may lead to insufficient bond resistance in the concrete structures. The behavior of reinforcing steel bars in UHPC is generally determined by conducting pull-out tests. However, these tests need amounts of time and cost. A soft computing technique is an alternative approach that exterminates the need to conduct pull-out tests. This study presents the application of artificial intelligence technique in predicting ultimate bond strength (UBS) between ultrahigh-performance concrete (UHPC) and steel reinforcement bars and performs uncertainty quantification of such values. A total of 333 pull-out test data points were collected. A hybrid machine learning (ML) model, improved eliminate particle swamp optimization hybridized artificial neural network (IEPANN) was developed to predict the UBS between UHPC and reinforcing bars. The efficacy of the IEPANN was evaluated against other ML models such as particle swamp optimization hybridized artificial neural network (PANN), support vector regression (SVR), and multiple linear regression (MLR). The IEPANN model was benchmarked against three design codes, including CEB-FIP, AS3600, EC2, and four empirical models. The result obtained based on R = 0.973, MAE = 1.880, RMSE = 2.595, MAPE = 7.708, and RI = 0.944 shows that the IEPANN model can predict UBS between UHPC and reinforcing steel bars with acceptable accuracy. The uncertainty of the UBS was quantified with geometrical and material configuration randomness based on Monte Carlo simulation. The simulation result shows that the yield strength of steel rebar is the most sensitive, while the embedment length is the least sensitive to the bond strength. Systematic assessment of uncertainties in UBS in the design of reinforced concrete members may lead to a higher level of confidence and enhance construction reliability.

On the Prediction of Uniaxial Tensile Behavior Beyond the Yield Point of Wrought and Additively Manufactured Ti-6Al-4V

In this paper, phenomenological relationships are presented that permit the prediction of the plastic regime of stress–strain curves using a limited number of parameters. These relationships were obtained from both conventional (wrought + β annealed) and additively manufactured (i.e., “3D printed”) Ti-6Al-4V. Three different methods of additive manufacturing have been exploited to produce the materials, including large-volume electron beam additive manufacturing, large-volume laser hot wire additive manufacturing, and small-volume selective laser melting. The general fundamental expressions are independent not only of the additive manufacturing process, but also of a wide variety of post-deposition heat treatments, however the coefficients are specific to material states. Thus, this work demonstrates that it is possible to predict not only the ultimate tensile strength, but also the full true stress, true strain curves, if certain parameters of the material are known. In general, the prediction of ultimate tensile strength are within 5% of the experimentally measured values across all additive manufacturing variants and subsequent heat treatments. The absolute values of ultimate tensile strength range from ~ 910 MPa to ~ 1170 MPa for the single alloy Ti-6Al-4V. Data representing 113 explicit samples are included in this work.

Study on axial compressive behavior of circular steel tube confined rubberized concrete stub columns

An innovative steel tube confined rubberized concrete column is presented in this research to promote waste rubber recycling and improve the mechanical properties of conventional concrete. The axial compressive behavior of circular steel tube confined rubberized concrete (STC-RuC) stub column specimens is experimentally investigated. Thirty-two specimens including twenty-eight STC-RuC specimens and four referenced circular steel tube confined conventional concrete (STCC) column specimens were fabricated and tested. Experimental variables included rubber particle size, volume replacement ratio of the rubber particles, steel tube thickness, and concrete compressive strength. The failure modes, ultimate strength, strain characteristics, rigidity, confinement effect, and ductility were evaluated. The tests showed the failure of the STC-RuC specimens under axial compression was by the core concrete shear failure and the steel tube local buckling. Also, the ultimate strength of STC-RuC specimens was directly proportional to the rubber particle size and steel tube thickness, and it decreased with the increasing volume replacement ratio. With the increasing volume replacement ratio in STC-RuC specimens, the ductility improved, whereas the rigidity decreased. The rigidity increased with the increasing rubber particle size; compared with concrete-filled steel tube (CFST), rubberized concrete-filled steel tube (RuCFST), and STCC stub column specimens, the STC-RuC specimens had better axial compression behavior with a smaller confinement factor. Further, the ultimate strength of STC-RuC specimens was evaluated using various calculation models. The simplified formulae for the prediction of ultimate strengths were also presented.

Cross-Section Stability and Design of Normal Strength and High Strength Steel I-Sections in Fire

The structural response and design of normal strength and high strength steel I-sections at elevated temperatures are investigated in this paper. The shell finite element models of steel I-section elements capable of replicating their behavior in fire are developed and validated against experimental results from the literature. The validated shell finite element models are then utilized to generate extensive structural performance data for steel I-sections, considering a broad range of plate slenderness values for cross-section elements, elevated temperature levels, cross-section aspect ratios as well as different loading conditions and normal strength and high strength steel grades. The accuracy of the existing methods provided in the European structural steel fire design standard EN 1993-1-2 and its upcoming version prEN 1993-1-2 for the ultimate strength predictions of normal strength and high strength steel cross-sections in fire is assessed. Scope for improvement is observed. Considering this, a new method for the ultimate strength predictions of normal strength and high strength steel sections at elevated temperatures is put forward. It is shown that the proposed method leads to more accurate ultimate strength predictions for normal strength and high strength steel I-sections in fire with a higher level of reliability relative to the existing design methods provided in EN 1993-1-2 and prEN 1993-1-2.

Improvement of weld details to avoid brittle fracture initiating at the toes of weld access hole of the beam end — prediction of ultimate strength of welded joint with conventional detail

Improvement of weld details to avoid brittle fracture initiating at the toes of weld access hole of the beam end — prediction of ultimate strength of welded joint with conventional detail

Axial compressive behavior of square steel tube confined rubberized concrete stub columns

An innovative square steel tube confined rubberized concrete column is proposed in this study to alleviate the pollution from waste rubber and improve the mechanical properties of conventional concrete. The axial compressive behavior of square steel tube confined rubberized concrete (Ru-STCC) stub column is experimentally studied. A total of thirty-two specimens, including four square steel tube confined concrete (STCC) stub columns and twenty-eight Ru-STCC stub columns, were fabricated and tested. The variables included rubber particle volume replacement ratio, steel tube thickness, rubber particle size and concrete strength grade. The failure mode, axial ultimate compressive strength, rigidity, ductility, strain characteristics, and confinement effects were analyzed. The results show that the failure modes of Ru-STCC specimens were the core concrete shear failure and steel tube buckling. Ultimate axial compressive strength and rigidity were found inversely proportional to the volume replacement ratio, When the volume replacement ratio reached 20%, the ultimate axial compressive strength and rigidity reduced by more than 20%. With the increase of steel tube thickness, the ultimate axial compressive strength increased by more than 10%. Concrete strength grade and rubber particle size have significant effects on rigidity and ductility, respectively. In addition, the differences between square concrete-filled in steel tube (CFST), rubberized concrete-filled in steel tube (Ru-CFST), STCC, and Ru-STCC stub columns were discussed by compressive strength and deformation capacity. The axial ultimate compressive strength of square Ru-STCC specimens was evaluated using various calculation models. The simplified formulae for the prediction of axial ultimate compressive strength were also presented.

Interval prediction of ultimate strength for laminated composite structures using back-propagation neural network

Interval prediction of ultimate strength for laminated composite structures using back-propagation neural network

Load balancing external post-tensioning for strengthening existing inverted-T bent caps

Inverted-T bent caps have been widely used to provide increased clearance beneath bridges. Bridges with inverted-T bent caps often experience a prevalence of cracking at the web-to-ledge interface. To prevent these cracks, ledges require sufficient capacity to enable the transfer of applied loads from the girder seats to the pier column. This experimental investigation develops a retrofit solution which uses externally installed post-tensioned strands to balance dead loads thereby improving the capacity of the in-service inverted-T bent caps. Based on experimental results, the solution successfully improved the load carrying capacity of the bents and restrained existing cracks. The cracking load estimation is investigated to evaluate serviceability of the inverted-T bent caps without and with the PT retrofit. Ultimate strength predictions calculated using code-specified sectional methods and limit analysis are compared and contrasted with the experimental observations to evaluate their accuracy in estimating the observed ultimate strength. Based on the experimental observations and analytical comparisons; design, repair, and retrofit recommendations are provided, including their limitations.

Machine learning model to predict tensile properties of annealed Ti6Al4V parts prepared by selective laser melting

Abstract In this work, an artificial neural network model is established to understand the relationship among the tensile properties of as-printed Ti6Al4V parts, annealing parameters, and the tensile properties of annealed Ti6Al4V parts. The database was established by collecting published reports on the annealing treatment of selective laser melting (SLM) Ti6Al4V, from 2006 to 2020. Using the established model, it is possible to prescribe annealing parameters and predict properties after annealing for SLM Ti-6Al-4V parts with high confidence. The model shows high accuracy in the prediction of yield strength (YS) and ultimate tensile strength (UTS). It is found that the YS and UTS are sensitive to the annealing parameters, including temperature and holding time. The YS and UTS are also sensitive to initial YS and UTS of as-printed parts. The model suggests that an annealing process of the holding time of fewer than 4 h and the holding temperature lower than 850°C is desirable for as-printed Ti6Al4V parts to reach the YS required by the ASTM standard. By studying the collected data of microstructure and tensile properties of annealed Ti6Al4V, a new Hall-Petch relationship is proposed to correlate grain size and YS for annealed SLM Ti6Al4V parts in this work. The prediction of strain to failure shows lower accuracy compared with the predictions of YS and UTS due to the large scattering of the experimental data collected from the published reports.

Prediction of mechanical properties of aluminium metal matrix hybrid composites synthesized using Stir casting process by Machine learning

In recent days, aluminium based hybrid metal matrix composites (Al-HMMC) is heavily demanding in aerospace and marine industry due to its enriched mechanical properties. Al7075 and Al6061 aluminium alloy are most widely used as a base matrix in Al-HMMC and stir casting method is an efficient process to manufacture them. The mechanical properties of Al-HMMC depend on material parameters and fabrication process parameters. Effecting parameters were identified based on a thorough literature review, and they include material parameters such as different types of matrix, reinforcements, and their weight percentages and sizes, as well as fabrication process (Stir Casting) parameters such as stirring speed, stirring time, and reinforcement mixing temperature. To achieve desirable results of hybrid composite materials from stir casting method, it requires high experimental cost and time. To overcome these limitations, machine learning and artificial intelligence are emerging tools, which utilize probabilistic and statistical methods to learn from the past experience based upon the experimental data to predict more accurate outcomes. In this work an attempt has been made to develop a machine learning model that can predict mechanical properties accurately. Experimental data of mechanical properties of aluminium HMMC has been collected and then used for training and testing of different machine learning models. Decision tree regression model showed maximum accuracy of 92.029 % on dataset for prediction of Ultimate Tensile strength (UTS) of Al-HMMC. The UTS of two samples of Al-HMMC with Al7075 and Al6061 as base matrix were predicted from decision tree model and compared with actual experimental values with an error < 10%.

Ultimate Bending Strength Evaluation of MVFT Composite Girder by using Finite Element Method and Machine Learning Regressors

This paper has evaluated the bending performance of a novel prefabricated MVFT steel-concrete composite girder. 9 meters pilot MVFT girder was analyzed by validated finite element model. In the pilot test, the height of web, the length of grouted concrete in the girder and net spacing between webs were parametrically modeled to discuss their effect to the bending strength. An ultimate bending strength formula has been obtained, which was based on the regression of parametric results. In the meantime, the two Machine Learning (ML) models, BP neural network and Least Squares Support Vector Machine, have been also implemented to train and then predict the ultimate strength of MVFT girder. Three factors were selected as input in ML models: the distance between steel girder’s Tensile Centroid(TC) and slab’s Compressive Centroid(CC), the distance between steel girder’s TC and its CC, the compressive area of steel girder. After the completion of the ML training, the ultimate strength predictions of 30 meters MVFT girder by BP model and the formula have been compared, which agrees well with each other and validates their accuracy.

Prediction of the Ultimate Strength of Notched and Unnotched IM7/977-3 Laminated Composites Using a Micromechanics Approach.

This paper proposes a multi-scale analysis technique based on the micromechanics of failure (MMF) to predict and investigate the damage progression and ultimate strength at failure of laminated composites. A lamina’s representative volume element (RVE) is developed to predict and calculate constituent stresses. Damages that occurred in the constituents are calculated using separate failure criteria for both fiber and matrix. Subsequently, the volume-based damage homogenization technique is utilized to prevent the localization of damage throughout the total matrix zone. The proposed multiscale analysis procedure is then used to investigate the notched and unnotched behavior of three multi-directional composite layups, [30, 60, 90, −60, 30]2S, [0, 45, 90, −45]2S, and [60, 0, −60]3S, subjected to static tension and compression loading. The specimen is fabricated from unidirectionally reinforced composite (IM7/977-3). The prediction of ultimate strength at failure and equivalent stiffness are then benchmarked against the experimental test data. The comparative analysis with various failure models is also carried out to validate the proposed model. MMF demonstrated the capability to correctly predict the ultimate strength at failure for a range of multidirectional composites laminates under tensile and compressive load. The numerically predicted findings revealed a good agreement with the experimental test data. Out of the three investigated composite layups, the simulated results for the quasi-isotropic [0, 45, 90, −45]2S layup agreed extremely well with the experimental results with all the percentage errors within 10% of the measured failure loads.