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2018, Iranian Journal of Science and Technology, Transactions of Civil Engineering
Journal of Constructional Steel Research, 2006
This study proposes Neural Networks (NN) as a new approach for the estimation and explicit formulation of available rotation capacity of wide flange beams. Rotation capacity is an important phenomenon which determines the plastic behaviour of steel structures. Thus the database for the NN training is directly based on extensive experimental results from literature. The results of the NN approach are compared with numerical results obtained by a specialized computer. Available rotation capacity is also introduced in a closed form solution based on the proposed NN model. The proposed NN method is seen to be more accurate than numerical results, practical and fast compared to FE models.
Civil Engineering and Architecture, 2024
Shear failures exhibit a brittle nature, often resulting in catastrophic collapse without sufficient advance warning or the capacity to redistribute internal stresses. Consequently, shear failures pose a greater risk and require more attention from structural engineers. It is crucial to incorporate preventive measures in structural design to avoid abrupt shear failures. The work presented in this article attempts to predict the shear strength of reinforced concrete beams as a complex structural engineering problem without the need for extra computational resources by employing the capabilities of Artificial Intelligence (AI) techniques. In recent decades, significant amounts of research have been done on the AI methods such as artificial neural networks (ANNs), fuzzy logic and genetic algorithms to predict the shear strength of RC beams. In this research, adaptive neuro-fuzzy inference system (ANFIS) and ANNs are developed to predict the shear capacity of RC beams. The required data in the form of major factors affecting the shear capacity of RC beams lacking stirrups are compressive strength of concrete, beam depth, effective width, shear span-to-depth ratio, proportion of longitudinal steel and the yield strength of the reinforced longitudinal steel have been considered in this study. Also, in the context of this investigation, a comparison was conducted between the techniques of ANNs and ANFIS. The outcomes demonstrated that both methods exhibited favourable predictive capabilities. Nevertheless, the ANFIS architecture proposed, which incorporates a hybrid learning algorithm, outperformed the multilayer feedforward ANN that utilizes the backpropagation algorithm. The findings indicated that ANFIS is a suitable technique for predicting intricate relationships between input and output parameters, thus making it a valuable tool in predicting the shear strength of RC beams.
Metals
Steel beams’ shear strength is one of the most important factors that influence how quickly webs buckle. Despite extensive studies having been performed over the previous three decades, the existing procedures did not achieve the necessary reliability to predict the ultimate shear resistance of plate girders. New techniques called Learner Techniques have started to be used over the last few years; these techniques were applied to calculate the steel beam shear strength. In this study, a Regression Learner Techniques model was built using data from 100 test results from previously published research. Based on the geometric and material properties of the web and flanges available in the published tests, a model was built using Artificial Neural Networks. Based on sensitivity analysis, a Cascade Forward Backpropagation Neural Networks (CFBNN) approach was utilized to anticipate the shear strength of steel beams. The proposed models outperformed current hybrid artificial intelligence mo...
Steel and Composite Structures, 2014
The flexural behaviour of steel beams significantly affects the structural performance of the steel frame structures. In particular, the flexural overstrength (namely the ratio between the maximum bending moment and the plastic bending strength) that steel beams may experience is the key parameter affecting the seismic design of non-dissipative members in moment resisting frames. The aim of this study is to present a new formulation of flexural overstrength factor for steel beams by means of artificial neural network (NN). To achieve this purpose, a total of 141 experimental data samples from available literature have been collected in order to cover different cross-sectional typologies, namely I-H sections, rectangular and square hollow sections (RHS-SHS). Thus, two different data sets for I-H and RHS-SHS steel beams were formed. Nine critical prediction parameters were selected for the former while eight parameters were considered for the latter. These input variables used for the development of the prediction models are representative of the geometric properties of the sections, the mechanical properties of the material and the shear length of the steel beams. The prediction performance of the proposed NN model was also compared with the results obtained using an existing formulation derived from the gene expression modeling. The analysis of the results indicated that the proposed formulation provided a more reliable and accurate prediction capability of beam overstrength.
WIT Transactions on Information and Communication Technologies, 2003
In the conception and design of civil engineering structures several factors should be considered: aesthetics, functionality, deformability, durability, resistance and cost. In general, that exercise is conditioned to the search of the safest solution at low cost. This concern, associated to the evolution of the materials' properties and of the computational tools, has been leading to the use of more and more refined design methods. In the special case of steel structures with very slender sections, the error presented by the current design formulas to forecasting the ultimate resistance of steel beams subjected to concentrated loads is significant, due to: the influence of several independent parameters in the behaviour; the insufficient number of experimental data that allows a parametric analysis; and the calibration of simplified models. While parametric analysis is intensive and hard work, the construction of models using Data Mining (DM) techniques in a Knowledge Discovery...
Complexity
The shear and bending are the actions that are experienced in the beam owing to the fact that the beam is a flexural member due to the load in the transverse direction to their longitudinal axis. The shear strength (Vs) computation of reinforced concrete (RC) beams has been a major topic in the field of structural engineering. There have been several methodologies introduced for the Vs prediction; however, the modeling accuracy is relatively low owing to the complex characteristic of the resistance mechanism involving dowel effect of longitudinal reinforcement, concrete in the compression zone, contribution of the stirrups if existed, and the aggregate interlock. Hence, the current research proposed a new soft computing model called random forest (RF) to predict Vs. Experimental datasets were collected from the open-source literature including the related geometric properties and concrete characteristics of beam specimens. Nine input combinations were constructed based on the statis...
Sustainability, 2023
Sustainable solutions in the building construction industry have emerged as a new method for retrofitting applications in the last two decades. Fiber-reinforced polymers (FRPs) have garnered much attention among researchers for improving reinforced concrete (RC) structures. The existing design guidelines for FRP-strengthened RC members were developed using empirical methods that are based on specific databases, limiting the accuracy of the predicted results. Therefore, the use of innovative and efficient prediction tools to predict the behavior of FRP-strengthened RC members has become essential. During the last few years, efforts have been progressively focused on the use of machine learning (ML) as a feasible and effective technique for solving various structural engineering problems. Its capability to predict the behavior of complex nonlinear structural systems while considering a wide range of parameters offers a distinctive opportunity to make the behavior of RC members more predictable and accurate. This paper aims to evaluate the current state of using various ML algorithms in RC members strengthened with FRP to enable researchers to determine the capabilities of current solutions as well as to find research gaps to carry out more research to bridge revealed knowledge and practice gaps. Scopus databases were searched using predefined standards. The search revealed ninety-six articles published between 2016 and 2023. Consequently, these articles were analyzed for ML applications in the field of FRP retrofitting, including flexural and shear strengthening of RC beams, flexural strengthening of slabs, confinement and compressive strength of columns, and FRP bond strength. The results reveal that 32% of the reviewed studies focused on the application of ML techniques to the flexural and shear strengthening of RC beams, 32% on the confinement and compressive strength of columns, 6.5% on the flexural strengthening of slabs, 22% on FRP bond strength, 6.5% on materials, and 1% on beam-column joints. This research also revealed that the application of various ML algorithms has shown a significant improvement in resistance prediction accuracy as compared with the existing empirical solutions. Supervised learning techniques were the most favorable learning method due to their good generalization, interpretability, adaptability, and predictive efficiency. In addition, the selection of suitable ML algorithms and optimization techniques is found to be mainly dictated by the nature of the problem and the characteristics of the dataset. Nonetheless, selecting the most appropriate ML model and optimization algorithm for each specific application remains a challenge, given that each algorithm is developed with different principles and methodologies.
SN Applied Sciences
The necessity of providing low-cost housing to economically weaker sections of society has been recognised by the national government of India. In mountainous areas, the use of locally available construction material, such as bamboo, as concrete reinforcement has increased due its easy availability and economic benefit. However, due to the inadequate codal provisions for the design and detailing of bamboo-reinforced structures, evaluating the serviceability criteria for their deflection behaviour under different loads is difficult. Furthermore, factors such as bond failure between reinforcement and concrete, shrinkage and corrosion of reinforcing material, and uncertainty in material strength make the prediction of deflection even more cumbersome. This study presents an artificial neural network (ANN)-based method modelled using MATLAB for predicting the deflection behaviour of three types of beams, namely plain, steel-reinforced, and bamboo-reinforced beams. Experimental investigation is conducted to record data at regular load increments for the aforementioned three beam typologies fabricated in the laboratory under two-point loading for 28 days. A total of 122 laboratory test data are recorded for modelling the ANN. The used approach involves predicting the relationship among the applied load, tensile strength of the reinforcement, percentage (amount) of reinforcement (taken as input), and deflection of the beam (obtained as output). The present ANN approach exhibits gives satisfactory performance (coefficient of determination (R 2) = 0.9983 and mean square error = 0.00049) in predicting the deflection behaviour of beams. Hence, the ANN approach can be used as an efficient and robust tool in predicting serviceability behavior of different types of reinforced concrete beams.
Thin-Walled Structures, 2015
Circular hollow section (CHS) steel beams are widely used in both mechanical and civil applications. CHS members are mainly subjected to bending. The flexural overstrength factor (namely the ratio between the ultimate bending strength over the plastic bending moment) characterizes the flexural behaviour of steel CHS beams. This paper describes an analytical study aiming to develop a new explicit formulation for predicting the flexural overstrength factor of steel CHS beams. The proposed models were derived from soft-computing techniques based on both neural networks (NNs) and gene expression programming (GEP), respectively. To this aim, experimental data available from scientific literature were analysed and collected to form a comprehensive dataset for developing the prediction models. A total number of 128 samples was considered in order to cover different geometric and mechanical properties. The input variables accounted for the modelling were the external diameter (D), wall thickness (t), shear length (L v ), and steel yield strength (f y ). The database was arbitrarily divided into two subsets to obtain both training and testing databases for the generation of the models. The prediction capability of the proposed formulations was assessed with respect to the experimental data and the levels of accuracy and performance were also compared with an existing analytical formulation available previously developed for cold-formed sections. The results showed that the novel proposed models derived from NN and GEP methods provide better prediction performances than those obtained by the existing analytical model.
Neural Computing and Applications, 2019
Effective stiffness of reinforced concrete (RC) members has a very important role in the performance evaluation of RC frame buildings through nonlinear dynamic analyses. The beam effective stiffness can be readily computed using mechanics, but the evaluation of column stiffness is a complicated process and the use of support vector regression helps in this regard. Therefore, in this study, an attempt is made to predict the effective stiffness ratio of reinforced concrete columns using support vector regression (SVR) approach. A data set of 208 samples, which are collected through nonlinear dynamic analysis of reinforced concrete buildings using SAP2000 software, is utilized to develop the SVR model. The input parameters considered are reinforcement percentage, axial load and depth of the column section in both the perpendicular directions, and the output parameter is the effective stiffness ratio of columns. Three different kernel parameters are used, namely exponential radial basis function (ERBF), Gaussian radial basis function and polynomial function for SVR modelling, among which ERBF is found to be the most suitable one. The obtained results indicate that the statistical performance of the SVR-ERBF model is better than the models with other two kernels in predicting the effective stiffness ratio of reinforced concrete columns. Performance of the SVR model is compared with the results of multi-variable regression analysis. In addition to that, a sensitivity analysis is also performed to check the influence of each input parameter on output responses. Keywords Support vector regression Á Effective stiffness ratio Á Reinforced concrete columns Á Nonlinear dynamic analysis Neural Computing and Applications
Neural Computing and Applications, 2015
In this study, new design equations were derived for the assessment of shear resistance of steel fiberreinforced concrete beams (SFRCB) utilizing multi-expression programming (MEP). The superiority of MEP over conventional statistical techniques is due to its ability in modeling of mechanical behavior without a need to predefine the model structure. The MEP models were developed using a comprehensive database obtained through an extensive literature review. New criteria were checked to verify the validity of the models. A sensitivity analysis was carried out and discussed. The MEP models provide good estimations of the shear strength of SFRCB. The developed models significantly outperform several equations found in the literature.
Procedia Structural Integrity, 2019
Despite the abundance of research works, both experimental and theoretical, conducted since the middle of the previous century up to today, the determination of the shear stress value is still remains an open issue of great interest in structural engineering. The need for further research is indicated by the fact that the majority of available proposals, whether proposed by regulatory agencies or various individuals researchers, lead to the estimation of different shear stress values; moreover, the comparison of estimated values with experimental values demonstrates that the available proposals lead to an overestimation or to an underestimation of the "true" shear stress. In this research study, the artificial neural networks approach is used to estimate the ultimate shear capacity of reinforced concrete beams with transverse reinforcement. More specifically, artificial neural network models have been examined for predicting the shear capacity of concrete beams, based on experimental test results available in the pertinent literature. The comparison of the consequent results with the corresponding experimental ones as well as with available formulas from previous research studies or code provisions makes obvious the ability of artificial neural networks to evaluate the shear capacity of reinforced concrete beams in a trustworthy and effective manner. Furthermore, the preliminary results presented in this work reveal the crucial parameters that affect the value of the shear strength of reinforced concrete beams with or without transverse reinforcement.
Construction and Building Materials, 2004
This paper presents the development of multilayer feedforward artificial neural network models for predicting the ultimate shear capacity of RC beams strengthened with web bonded steel plates. Two models are constructed using the data obtained from FEM model previously developed and validated by the authors. It is found that the neural network models predict the shear capacities of beams quite accurately. The model with dimensionless parameters is found to be slightly less accurate than the ordinary model. Moreover, the neural network models predict the shear capacities of beams more accurately than the formula proposed by the authors in a previous study. Limited parametric studies show that the network models capture the underlying shear behavior of RC beams with web-bonded steel plates quite accurately. ᮊ
Expert Systems with Applications, 2009
This paper presents the application of soft computing techniques for strength prediction of heat-treated extruded aluminium alloy columns failing by flexural buckling. Neural networks (NN) and genetic programming (GP) are presented as soft computing techniques used in the study. Gene-expression programming (GEP) which is an extension to GP is used. The training and test sets for soft computing models are obtained from experimental results available in literature. An algorithm is also developed for the optimal NN model selection process. The proposed NN and GEP models are presented in explicit form to be used in practical applications. The accuracy of the proposed soft computing models are compared with existing codes and are found to be more accurate. j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e s w a v nondimensional column design strength A. Cevik et al. / Expert Systems with Applications 36 (2009) 6332-6342 6333 * Bold sets are used as test sets for NN and GEP training. RAS 1 : proposed formulation by Rasmussen [4] for a = 0.4. RAS 2 : proposed formulation by Rasmussen [4] for a = 0.3. A. Cevik et al. / Expert Systems with Applications 36 (2009) 6332-6342
Journal of Science and Technology (Ghana), 2012
Applied Sciences
In this paper, an optimization approach was presented for the flexural strength and stiffness design of reinforced concrete beams. Surrogate modeling based on machine learning was applied to predict the responses of the structural system in three-point flexure tests. Three design input variables, the area of steel bars in the compression zone, the area of steel bars in the tension zone, and the area of steel bars in the shear zone, were adopted for the dataset and arranged by the Box-Behnken design method. The dataset was composed of thirteen specimens of reinforced concrete beams. The specimens were tested under three-points flexure loading at the age of 28 days and both the failure load and the maximum deflection values were recorded. Compression and tension tests were conducted to obtain the concrete data for the analysis and numerical modeling. Afterward, finite element modeling was performed for all the specimens using the ATENA program to verify the experimental tests. Subsequ...
Engineering Structures, 2018
In this research, a new Support Vector Regression algorithm coupled with Particle Swarm Optimization (SVR-PSO) is developed to predict the shear strength (S s) of steel fiber-reinforced concrete beams (SFRC) using several input combinations denoting the dimensional and material properties. The experimental test data are collected from reliable literature sources. The main variables used to construct the predictive model are related to the dimensional and material properties of the beams. SVR-PSO, the objective predictive model, is validated against a classical neural network model tuned with the same metaheuristic optimizer algorithm. The findings of the modeling study provide a clear evidence of the superior capability of the SVR-PSO used to predict the SFRC shear strength relative to the benchmark model. In addition, the construction of the predictive models with a lesser number of input data attributes are attained, leading an acceptable prediction accuracy of the SVR-PSO compared to the ANN-PSO model. In summary, the proposed SVR-PSO methodology has demonstrates an effective engineering strategy that can be applied in problems of structural and construction engineering prospective, applied to predict shear strength of steel fiber reinforced concrete beam using advanced hybrid artificial intelligence models developed in this study.
Journal of Constructional Steel Research, 2011
In this paper, load carrying capacity of simply supported castellated steel beams, susceptible to webpost buckling, is studied. The accuracy of the nonlinear finite element (FE) method to evaluate the load carrying capacity and failure mode of the beams is discussed. In view of the high computational burden of the nonlinear finite element analysis, a parametric study is achieved based on FE and an empirical equation is proposed to estimate the web-posts' buckling critical load of the castellated steel beams. Also as other alternatives to achieve this task, the traditional back-propagation (BP) neural network and adaptive neuro-fuzzy inference system (ANFIS) are employed. In this case, the accuracy of the proposed empirical equation, BP network and ANFIS are examined by comparing their provided results with those of conventional FE analysis. The numerical results indicate that the best accuracy associates with the ANFIS and the neural network models provide better accuracy than the proposed equations.
The possibility of consuming artificial neural networks (ANN) using Matlab software to calculate the rotational capacity of steel cold-formed C-and Z-section purlins. Rotational capacity is a significant phenomenon as in the situation of steel purlins which are extensively used in roofing wide-ranging buildings. The complex conducts of such members make the conventional design approaches not satisfactory from a reliability standpoint. The main aim of this paper was to give a quick and precise technique for estimating local buckling capacity of C-and Z-section purlin. Good agreement was attained concerning (ANN) technics outcomes and data from literature. Trained neural network develops easy to-utilize method for calculating yielding and ultimate moment's capacity of C-and Z-section. Broad parametric investigations were additionally performed and introduced graphically to analyse the impact of geometric and mechanical properties on rotational capacity. It was found that the proposed (ANN) based technics is practical in predicting both the yield and buckling rotational strength of cold-formed purlin steel sections.
SN applied sciences, 2020
This paper aims at establishing a framework for the development of artificial neural networks (ANNs) capable of realistically predicting the load-carrying capacity of reinforced concrete (RC) members. Multilayer back propagation neural networks are developed through the use of MATLAB and enriched databases which contain information describing the variation of load-carrying capacity in relation to key design parameters associated with the RC specimens (i.e. beams) considered. This work forms the basis for the development of a knowledge-based structural analysis tool capable of predicting RC structural response. A detailed discussion is provided on the different aspects of the proposed framework which include (1) the formation and analysis of the relevant (experimental) data, (2) the architecture of the ANNs, (3) the training/calibration process they undergo and finally, (4) ways of extending their applicability enabling them to predict the behaviour of RC structural forms with design parameters not represented in the available experimental database. Non-linear finite element analysis is used for validating the predictions of the ANN models developed. The comparative study reveals that the ANN models developed through the proposed framework are capable of effectively predicting the load-carrying capacity s of the RC structural forms considered quickly, accurately and without requiring significant computational resources. Keywords Artificial neural network • Database • Sampling method • Ultimate limit state • Reinforced concrete • Training process • Finite element analysis • Failure • Latin hypercube sampling List of symbols v Shear span b Width of the beam specimen cross-section d Effective depth of the beam specimen cross-section A s Area of longitudinal reinforcement acting in tension A sw Area of transverse reinforcement v ∕d Shear span to depth ratio f c Uniaxial compressive strength of concrete f yl Yield stress of longitudinal reinforcement bars f yw Yield stress of transverse reinforcement bars s Spacing between shear links l Ratio of tensile reinforcement (l = A s ∕b ⋅ d) w Ratio of transverse reinforcement (l = A sw ∕b ⋅ s) V u Shear strength Abbreviations CFP Compressive force path ANN Artificial neural network ULS Ultimate limit state LHS Latin hypercube sampling * Afaq Ahmad,