Nonlinear finite element and machine learning modeling of tubed reinforced concrete columns under eccentric axial compression loading

Isleem, Haytham F.; Chukka, Naga Dheeraj Kumar Reddy; Bahrami, Alireza; Kumar, Rakesh; Sor, Nadhim Hamah

doi:10.1016/j.aej.2024.02.026

Cited by 7 publications

(2 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A key component of supervised learning, the decision tree (DT) provides a flexible method for addressing problems related to both regression and classification 38 , 40 , 41 . Its hierarchical structure contains complex classifications, with each node denoting a crucial dataset attribute.…”

Section: Machine Learning Methodologymentioning

confidence: 99%

“…These techniques are essentially computer algorithms that improve automatically based on prior experience and measured data. Due to its ability to analyse high-dimensional variables and address variable collinearity, ML has found widespread use in addressing the limitations of traditional methods for predicting concrete performance 37 – 40 . Young et al 41 proposed a method for predicting the performance of industrial concrete.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning

Sathvik,

Kumar,

Ulloa

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

India’s cement industry is the second largest in the world, generating 6.9% of the global cement output. Polycarbonate waste ash is a major problem in India and around the globe. Approximately 370,000 tons of scientific waste are generated annually from fitness care facilities in India. Polycarbonate waste helps reduce the environmental burden associated with disposal and decreases the need for new raw materials. The primary variable in this study is the quantity of polycarbonate waste ash (5, 10, 15, 20 and 25% of the weight of cement), partial replacement of cement, water-cement ratio and aggregates. The mechanical properties, such as compressive strength, split tensile strength and flexural test results, of the mixtures with the polycarbonate waste ash were superior at 7, 14 and 28 days compared to those of the control mix. The water absorption rate is less than that of standard concrete. Compared with those of conventional concrete, polycarbonate waste concrete mixtures undergo minimal weight loss under acid curing conditions. Polycarbonate waste is utilized in the construction industry to reduce pollution and improve the economy. This study further simulated the strength characteristics of concrete made with waste polycarbonate ash using least absolute shrinkage and selection operator regression and decision trees. Cement, polycarbonate waste, slump, water absorption, and the ratio of water to cement were the main components that were considered input variables. The suggested decision tree model was successful with unparalleled predictive accuracy across important metrics. Its outstanding predictive ability for split tensile strength (R2 = 0.879403), flexural strength (R2 = 0.91197), and compressive strength (R2 = 0.853683) confirmed that this method was the preferred choice for these strength predictions.

show abstract

Section: Machine Learning Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning

Sathvik,

Kumar,

Ulloa

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

Prediction of mechanical properties of high‐performance concrete and ultrahigh‐performance concrete using soft computing techniques: A critical review

Kumar,

Rai,

Samui

2024

Structural Concrete

View full text Add to dashboard Cite

A cement‐based material that meets the general goals of mechanical properties, workability, and durability as well as the ever‐increasing demands of environmental sustainability is produced by varying the type and quantity of individual constituents in high‐performance concrete (HPC) and ultrahigh‐performance concrete (UHPC). Expensive and time‐consuming laboratory experiments can be used to estimate the properties of concrete mixtures and elements. As an alternative, these attributes can be approximated by means of predictive models created through the application of artificial intelligence (AI) methodologies. AI approaches are among the most effective ways to solve engineering problems due to their capacity for pattern recognition and knowledge processing. Machine learning (ML) and deep learning (DL) are a subfield of AI that is gaining popularity across many scientific domains as a result of its many benefits over statistical and experimental models. These include, but are not limited to, better accuracy, faster performance, greater responsiveness in complex environments, and lower economic costs. In order to assess the critical features of the literature, a comprehensive review of ML and DL applications for HPC and UHPC was conducted in this study. This paper offers a thorough explanation of the fundamental terms and ideas of ML and DL algorithms that are frequently used to predict mechanical properties of HPC and UHPC. Engineers and researchers working with construction materials will find this paper useful in helping them choose accurate and appropriate methods for their needs.

show abstract

Prediction of compressive strength of high-volume fly ash self-compacting concrete with silica fume using machine learning techniques

Kumar,

Rai

et al. 2024

Construction and Building Materials

View full text Add to dashboard Cite

Nonlinear finite element and machine learning modeling of tubed reinforced concrete columns under eccentric axial compression loading

Cited by 7 publications

References 55 publications

Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning

Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning

Prediction of mechanical properties of high‐performance concrete and ultrahigh‐performance concrete using soft computing techniques: A critical review

Prediction of compressive strength of high-volume fly ash self-compacting concrete with silica fume using machine learning techniques

Contact Info

Product

Resources

About