Modeling of strength of high-performance concrete using artificial neural networks

By I-Cheng Yeh. 1998

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, methodology): “This data set, available at the UCI Machine Learning Repository (Bache and Lichman, 2013), concerns a systematic study (Yeh, 1998) regarding concrete compressive strength as a function of ingredients and age.”
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond (2020, background): “In an engineering setting, the input could be the composition of some compound material, and the output could be a prediction of its strength [156] or stability.”
Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization (2020, unknown): “Some of the recent applications of DL approaches on material science include material classiﬁcation [35, 36], defect classiﬁcation [37–39], microstructure identiﬁcation [40, 41], microstructure reconstruction [42, 43], composite strength prediction [44], etc.”
Computational design optimization of concrete mixtures: A review (2018, methodology): “Since then, researchers have applied ANN methods to wide-ranging problems in cement and concrete research; they have been used to model several types of concrete properties, including compressive strength, slump, filling capacity, and segregation and for many types of concrete including high perform”
A General Method for Visualizing and Explaining Black-Box Regression Models (2011, background): “The compression strength is a highly non-linear problem [2].”
Expert Systems With Applications (unknown): “The next example, Concrete Strength ( Yeh, 1998 ), studies how compressive strength of different concrete are affected by attributes of the concretes.”
Optimal ratio for data splitting (2022, background): “Consider the concrete compressive strength dataset from Yeh (1998) which can be obtained from the UCI Machine Learning Repository (Dua and Graﬀ, 2017).”