Predicting compressive strength of hollow concrete prisms using machine learning techniques and explainable artificial intelligence (XAI)

Author:

Bin Inqiad WaleedORCID,Dumitrascu Elena Valentina,Dobre Robert AlexandruORCID,Khan Naseer Muhammad,Hammood Abbas Hussein,Henedy Sadiq N.,Khan Rana Muhammad Asad

Funder

National University of Science and Technology POLITEHNICA Bucharest

Publisher

Elsevier BV

Reference153 articles.

1. P. G. Asteris et al.,“Masonry Compressive Strength Prediction Using Artificial Neural Networks.”.

2. H. B. Kaushik, ; Durgesh, C. Rai, S. K. Jain, and M. Asce, “Stress-Strain Characteristics of Clay Brick Masonry under Uniaxial Compression”, doi: 10.1061/ASCE0899-1561200719:9728.

3. Reinforced moment-resisting glulam bolted connection with coupled long steel rod with screwheads for modern timber frame structures;Shu;Earthq. Eng. Struct. Dynam.,2023

4. Validation of Analytical and Continuum Numerical Methods for Estimating the Compressive Strength of Masonry;Lourenço,2006

5. Effects of the position and chloride-induced corrosion of strand on bonding behavior between the steel strand and concrete;Li;Structures,2023

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