Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups

A new design equation is proposed for the prediction of shear strength of reinforced concrete (RC) beams without stirrups using an innovative linear genetic programming methodology. The shear strength was formulated in terms of several effective parameters such as shear span to depth ratio, concrete cylinder strength at date of testing, amount of longitudinal reinforcement, lever arm, and maximum specified size of coarse aggregate. A comprehensive database containing 1938 experimental test results for the RC beams was gathered from the literature to develop the model. The performance and validity of the model were further tested using several criteria. An efficient strategy was considered to guarantee the generalization of the proposed design equation. For more verification, sensitivity and parametric analysis were conducted. The results indicate that the derived model is an effective tool for the estimation of the shear capacity of members without stirrups (R = 0.921). The prediction performance of the proposed model was found to be better than that of several existing buildings codes.     

Empirical modeling of shear strength of steel fiber reinforced concrete beams by gene expression programming

The addition of steel fibers into concrete improves the postcracking tensile strength of hardened concrete and hence significantly enhances the shear strength of reinforced concrete reinforced concrete beams. However, developing an accurate model for predicting the shear strength of steel fiber reinforced concrete (SFRC) beams is a challenging task as there are several parameters such as the concrete compressive strength, shear span to depth ratio, reinforcement ratio and fiber content that affect the ultimate shear resistance of FRC beams. This paper investigates the feasibility of using gene expression programming (GEP) to create an empirical model for the ultimate shear strength of SFRC beams without stirrups. The model produced by GEP is constructed directly from a set of experimental results available in the literature. The results of training, testing and validation sets of the model are compared with experimental results. All of the results show that GEP model is fairly promising approach for the prediction of shear strength of SFRC beams. The performance of the GEP model is also compared with different proposed formulas available in the literature. It was found that the GEP model provides the most accurate results in calculating the shear strength of SFRC beams among existing shear strength formulas. Parametric studies are also carried out to evaluate the ability of the proposed GEP model to quantitatively account for the effects of shear design parameters on the shear strength of SFRC beams.     

Predictive model for shear strength estimation in reinforced concrete beams with recycled aggregates using Gaussian process regression

Neural Computing and Applications - In order to attain sustainable development, recycled concrete aggregates (RCAs) are increasingly utilized in civil engineering projects. Therefore, it is vital...     

Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R²) during the testing phase (R² = 0.95), whereas the other comparable models generated relatively lower values of R² ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.

     

Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete

In this study, an artificial neural network (ANN) and fuzzy logic (FL) study were developed to predict the compressive strength of silica fume concrete. A data set of a laboratory work, in which a total of 48 concretes were produced, was utilized in the ANNs and FL study. The concrete mixture parameters were four different water–cement ratios, three different cement dosages and three partial silica fume replacement ratios. Compressive strength of moist cured specimens was measured at five different ages. The obtained results with the experimental methods were compared with ANN and FL results. The results showed that ANN and FL can be alternative approaches for the predicting of compressive strength of silica fume concrete.     

Designing new hybrid artificial intelligence model for CFST beam flexural performance prediction

Engineering with Computers - A substantial number of experimental studies have reported on the flexural performance of concrete-filled steel tube (CFST) beams. Due to the problem complexity,...     

Prediction of shear strength of FRP-reinforced concrete beams without stirrups based on genetic programming

The use of fibre reinforced polymer (FRP) bars to reinforce concrete structures has received a great deal of attention in recent years due to their excellent corrosion resistance, high tensile strength, and good non-magnetization properties. Due to the relatively low modulus of elasticity of FRP bars, concrete members reinforced longitudinally with FRP bars experience reduced shear strength compared to the shear strength of those reinforced with the same amounts of steel reinforcement. This paper presents a simple yet improved model to calculate the concrete shear strength of FRP-reinforced concrete slender beams (a/d > 2.5) without stirrups based on the gene expression programming (GEP) approach. The model produced by GEP is constructed directly from a set of experimental results available in the literature. The results of training, testing and validation sets of the model are compared with experimental results. All of the results show that GEP is a strong technique for the prediction of the shear capacity of FRP-reinforced concrete beams without stirrups. The performance of the GEP model is also compared to that of four commonly used shear design provisions for FRP-reinforced concrete beams. The proposed model produced by GEP provides the most accurate results in calculating the concrete shear strength of FRP-reinforced concrete beams among existing shear equations provided by current provisions. A parametric study is also carried out to evaluate the ability of the proposed GEP model and current shear design guidelines to quantitatively account for the effects of basic shear design parameters on the shear strength of FRP-reinforced concrete beams.     

Using artificial intelligence models for the prediction of surface wear based on surface isotropy levels

Currently, a key industrial challenge in friction processes is the prediction of surface roughness and loss of mass under different machining processes, such as Electro-Discharge Machining (EDM), and turning and grinding processes. Under industrial conditions, only the sliding distance is easily evaluated in friction processes, while the acquisition of other variables usually implies expensive costs for production centres, such as the integration of sensors in functioning machine-tools. Besides, appropriate datasets are usually very small, because the testing of different friction conditions is also expensive. These two restrictions, small datasets and very few inputs, make it very difficult to use Artificial Intelligence (AI) techniques to model the industrial problem. So, the use of the isotropy level of the surface structure is proposed, as another input that is easily evaluated prior to the friction process. In this example, the friction processes of a cubic sample of 102Cr6 (40 HRC) steel and a further element made of X210Cr12 (60 HRC) steel are considered. Different artificial intelligence techniques, such as artificial regression trees, multilayer perceptrons (MLPs), radial basis networks (RBFs), and Random Forest, were tested considering the isotropy level as either a nominal or a numeric attribute, to evaluate improvements in the accuracy of surface roughness and loss-of-mass predictions. The results obtained with real datasets showed that RBFs and MLPs provided the most accurate models for loss of mass and surface roughness prediction, respectively. MLPs have slightly higher surface prediction accuracy than Random Forest, although MLP models are very sensitive to the tuning of their parameters (a small mismatch between the learning rate and the momentum in the MLP will drastically reduce the accuracy of the model). In contrast, Random Forest has no parameter to be tuned and its prediction is almost as good as MLPs for surface roughness, so Random Forest will be more suitable for industrial use where no expert in AI model tuning is available. Moreover, the inclusion of the isotropy level in the dataset, especially as a numeric attribute, greatly improved the accuracy of the models, in some cases, by up to 52% for MLPs, and by a smaller proportion of 16% in the Random Forest models in terms of Root Mean Square Error. Finally, Random Forest ensembles only trained with low and very high isotropy level experimental datasets generated reliable models for medium levels of isotropy, thereby offering a solution to reduce the size of training datasets.     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

     

Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams

Engineering with Computers - This study presents a novel artificial intelligence (AI) technique based on two support vector machine (SVM) models and symbiotic organisms search (SOS) algorithm,...     

Evaluation of material models for reinforced concrete structures

Capabilities of the concrete material model in ADINA are briefly summarized. The objective in this work was to evaluate the concrete model and make necessary enhancements to improve the nonlinear concrete analysis capabilities. The results of various sample analyses are given to demonstrate validity of the improved concrete model. These analyses involve small-scale cylinder tests as well as larger reinforced and prestressed concrete structures.     

New approach to optimization of reinforced concrete beams

The present paper outlines an application of genetic algorithm based strategies to a class of optimization tasks associated with the design of steel reinforced concrete structures. In this particular case, the principal design objective is to minimize the total cost of a structure. The resulting structure, however, should not only be marked with a low price but also comply with all strength and serviceability requirements for a given level of the applied load. To solve such a complex optimization problem with a number constraints calls for an efficient and yet reliable optimization technique. Here, the problem is addressed with the help of the augmented simulated annealing method. As an example, a simple continuous steel reinforced beam is analyzed to assess applicability of the proposed approach.     

A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

Engineering with Computers - Recycled aggregate concrete is used as an alternative material in construction engineering, aiming to environmental protection and sustainable development. However, the...     

Prediction of deflection of reinforced concrete shear walls

Reinforced concrete shear walls are used in tall buildings for efficiently resisting lateral loads. Due to the low tensile strength of concrete, reinforced concrete shear walls tend to behave in a nonlinear manner with a significant reduction in stiffness, even under service loads. To accurately assess the lateral deflection of shear walls, the prediction of flexural and shear stiffness of these members after cracking becomes important. In the present study, an iterative analytical procedure which considers the cracking in the reinforced concrete shear walls has been presented. The effect of concrete cracking on the stiffness and deflection of shear walls have also been investigated by the developed computer program based on the iterative procedure. In the program, the variation of the flexural stiffness of a cracked member has been evaluated by ACI and probability-based effective stiffness model. In the analysis, shear deformation which can be large and significant after development of cracks is also taken into account and the variation of shear stiffness in the cracked regions of members has been considered by using effective shear stiffness model available in the literature. Verification of the proposed procedure has been confirmed from series of reinforced concrete shear wall tests available in the literature. Comparison between the analytical and experimental results shows that the proposed analytical procedure can provide an accurate and efficient prediction of both the deflection and flexural stiffness reduction of shear walls with different height to width ratio and vertical load. The results of the analytical procedure also indicate that the percentage of shear deflection in the total deflection increases with decreasing height to width ratio of the shear wall.     

A simple genetic algorithm for the design of reinforced concrete beams

We present an optimization model for the design of rectangular reinforced concrete beams subject to a specified set of constraints. Our model is more realistic than previously published models because it minimizes the cost of the beam on strength design procedures, while also considering the costs of concrete, steel and shuttering. Thus our method leads to very practical designs. As there is an infinite number of possible beam dimensions and reinforcement ratios that yield the same moment of resistance, an efficient search technique is preferred over the more traditional iterative methods. We employ a simple genetic algorithm as the search engine, and we compare our results with those obtained via geometric programming. Since the adjustment of parameters in a genetic algorithm (e.g., population size, crossover and mutation rates, and maximum number of generations) is a significant problem for any application, we present our own methodology to deal with this problem. A prototype of this system is currently being tested in México, in order to evaluate its potential as a reliable design tool for real world applications.     

人工智能在计算机网络技术中的应用分析

随着信息技术的快速发展,人工智能已成为引领新一轮科技革命和产业变革的战略性技术。现阶段,各个国家都在争先布局和发展人工智能,以期能在未来科技革命中抢占高点和先机。人工智能是一种模拟人脑工作的技术形式,它包含系统推荐、人工神经网络、语言处理、机器学习等方面的内容。将人工智能应用于计算机网络技术,可以节省人力资源、提升效率,可较好地弥补当前计算机网络技术在运用过程中存在的不足,进一步提升计算机网络技术水平。     

Weight optimization for flexural reinforced concrete beams with static nonlinear response

The weight optimization of reinforced concrete (RC) beams with material nonlinear response is formulated as a general nonlinear optimization problem. Incremental finite element procedures are used to integrate the structural response analysis and design sensitivity analysis in a consistent manner. In the finite element discretization, the concrete is modelled by plane stress elements and steel reinforcement is modelled by discrete truss elements. The cross-sectional areas of the steel and the thickness of the concrete are chosen as design variables, and design constraints can include the displacement, stress and sizing constraints. The objective function is the weight of the RC beams. The optimal design is performed by using the sequential linear programming algorithm for the changing process of design variables, and the gradient projection method for the calculations of the search direction. Three example problems are considered. The first two are demonstrated to show the stability and accuracy of the approaches by comparing previous results for truss and plane stress elements, separately. The last one is an example of an RC beam. Comparative cost objective functions are presented to prove the validity of the approach.     

Efficient finite element modelling of reinforced concrete beams retrofitted with fibre reinforced polymers

This paper presents a new simple and efficient two-dimensional frame finite element (FE) able to accurately estimate the load-carrying capacity of reinforced concrete (RC) beams flexurally strengthened with externally bonded fibre reinforced polymer (FRP) strips and plates. The proposed FE, denoted as FRP–FB-beam, considers distributed plasticity with layer-discretization of the cross-sections in the context of a force-based (FB) formulation. The FRP–FB-beam element is able to model collapse due to concrete crushing, reinforcing steel yielding, FRP rupture and FRP debonding.The FRP–FB-beam is used to predict the load-carrying capacity and the applied load-midspan deflection response of RC beams subjected to three- and four-point bending loading. Numerical simulations and experimental measurements are compared based on numerous tests available in the literature and published by different authors. The numerically simulated responses agree remarkably well with the corresponding experimental results. The major features of this frame FE are its simplicity, computational efficiency and weak requirements in terms of FE mesh refinement. These useful features are obtained together with accuracy in the response simulation comparable to more complex, advanced and computationally expensive FEs. Thus, the FRP–FB-beam is suitable for efficient and accurate modelling and analysis of flexural strengthening of RC frame structures with externally bonded FRP sheets/plates and for practical use in design-oriented parametric studies.     

人工智能时代计算机专业人才的培养

人工智能时代需要计算机专业复合型人才,本文从专业课程体系、教学模式、实践能力培养等方面展开探讨,提出构建以人工智能为核心的课程群,因材施教、实施个性化教学模式,高校联合企业共同培养人才等来进行教学改革.     

探讨人工智能在计算机网络技术中的应用

人工智能开创了新的技术研究领域,并在各个行业中得到了广泛的应用。伴随着科学研究方法的改变,计算机的主要功能从数值计算向问题求解以及知识处理方向上发展,在这一转变过程中,人工智能是实现计算机的功能转变的核心技术。文章就人工智能技术进行了简单介绍,从计算机网络技术存在的问题分析中强调人工智能应用的重要性,并重点阐述了人工智能在计算机网络技术中的应用。