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

Gandomi, Amir H.; Mohammadzadeh, Saeed; Ordóñez, Juan Luis Pérez; Alavi, Amir H.

doi:10.1016/j.asoc.2014.02.007

Cited by 66 publications

(24 citation statements)

References 29 publications

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“…Gandomi et al [24] proposed a new design equation for the prediction of shear strength of reinforced concrete beams without stirrups using a 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 the date of testing, amount of longitudinal reinforcement, lever arm, and maximum specified size of coarse aggregate.…”

Section: Strength Models Of Concrete Using Machine Learning Methodsmentioning

confidence: 99%

A Comparative Study of Machine Learning Methods for Compressive Strength of Concrete

Silva¹,

Moita²

2019

World Congress on Civil, Structural, and Environmental Engineering

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This paper introduces a comparative study for the compressive strength of concrete by employing machine learning approaches such as Genetic Programming (GP) and Artificial Neural Network (ANN). The simulation of concrete strength is strongly needed to better understand its behaviours under different conditions and loads. Since many studies predict the comprehensive strength of conventional concrete from hardened characteristics, based on the data points gathered from different experimental tests, empirical models have been developed and verified in the past years. Proposed models are more reliable if the numbers of tests increase and their repeatability increase as well. However, these models are designed for a specific range of concrete strengths. On the other hand, numerical models are more reliable since they are devised based on theoretical rules which could consider behaviours of concrete under different loading paths. But, the validation of these models is made by different loading paths with different configurations that result in costly experiments and both models use only principal stresses and strains in their formulation. Employing machine learning approaches instead of traditional models makes it possible to develop a better understanding of the compressive strength of concrete. Hence, the focus of this paper is the application of machine learning process and their suitability to model concrete compressive strength compared with early models obtained from the literature and compared with some conventional approaches.

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Section: Strength Models Of Concrete Using Machine Learning Methodsmentioning

confidence: 99%

A Comparative Study of Machine Learning Methods for Compressive Strength of Concrete

Silva¹,

Moita²

2019

World Congress on Civil, Structural, and Environmental Engineering

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“…Furthermore, Cc was measured using an oedometer test based on ASTM D2435-11 [38]. In addition, seven consolidation test results conducted by Malih [39] were integrated into the laboratory database to make it more robust. The descriptive statistics of influential input parameters (i.e., LL, PL, and e0) and the output parameter, i.e., Cc, based on the database utilized for our study is presented in Table 1.…”

Section: Data Collectionmentioning

confidence: 99%

Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model

et al. 2019

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In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R 2 ), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R 2 , RMSE, and MAE compared to the other models.

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“…Random forests (RFs) have been utilized to predict building energy consumption (Ahmad, Mourshed, & Rezgui, ). Genetic programming has been applied to predict the shear strength of RC beams without stirrups (Gandomi, Mohammadzadeh, Pérez‐Ordóñez, & Alavi, ) and forecast the base shear of steel frame structures (Aminian, Javid, Asghari, Gandomi, & Esmaeili, ). Evolutionary polynomial regression (EPR) was used to estimate the shear capacity of RC beams without stirrups (Fiore, Quaranta, Marano, & Monti, ) and to model the mechanical behavior of rubber concrete (Ahangar‐Asr, Faramarzi, Javadi, & Giustolisi, ).…”

Section: Literature Reviewmentioning

confidence: 99%

A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments

Luo

Paal

2019

Computer aided Civil Eng

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Drift capacity of reinforced concrete (RC) columns is an important indicator to quantify the seismic vulnerability of RC frame buildings; however, it is challenging to accurately predict this value as the nonlinear behavior can vary greatly by column type. This article proposes a novel, local machine learning (ML) model, called locally weighted least squares support vector machines for regression (LWLS‐SVMR), which integrates LS‐SVMR and locally weighted training criteria to enhance and generalize the prediction of the drift capacity of RC columns, regardless of the type. A database of 160 circular RC columns covering flexure‐, shear‐, and flexure–shear‐critical specimens was developed to train and test the proposed LWLS‐SVMR. The proposed LWLS‐SVMR was validated by comparison with popular existing global and local learning approaches as well as a traditional empirical equation, and the results demonstrated that the proposed LWLS‐SVMR is superior to all other approaches and thus, is a promising artificial intelligence technique for enhancing the prediction of drift capacity, universally across RC flexure‐, shear‐, and flexure–shear‐critical columns. The LWLS‐SVMR exhibits capabilities which may yield it a feasible approach to predict complex, nonlinear behavior in a broad‐spectrum manner.

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

Cited by 66 publications

References 29 publications

A Comparative Study of Machine Learning Methods for Compressive Strength of Concrete

A Comparative Study of Machine Learning Methods for Compressive Strength of Concrete

Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model

A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments

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