Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences
Artificial intelligence (AI) and Machine learning (ML) train machines to achieve a high level
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …
Prediction of shear strength of soft soil using machine learning methods
Shear strength of the soil is an important engineering parameter used in the design and
audit of geo-technical structures. In this research, we aim to investigate and compare the …
audit of geo-technical structures. In this research, we aim to investigate and compare the …
Data-driven estimation models of asphalt mixtures dynamic modulus using ANN, GP and combinatorial GMDH approaches
D Rezazadeh Eidgahee, H Jahangir, N Solatifar… - Neural Computing and …, 2022 - Springer
The objective of the present study is to develop and evaluate machine learning-based
prediction models, employing the artificial neural networks (ANNs), Genetic Programming …
prediction models, employing the artificial neural networks (ANNs), Genetic Programming …
[HTML][HTML] Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network
BR Murlidhar, H Nguyen, J Rostami, XN Bui… - Journal of Rock …, 2021 - Elsevier
In mining or construction projects, for exploitation of hard rock with high strength properties,
blasting is frequently applied to breaking or moving them using high explosive energy …
blasting is frequently applied to breaking or moving them using high explosive energy …
Prediction of resilient modulus of ballast under cyclic loading using machine learning techniques
B Indraratna, DJ Armaghani, AG Correia, H Hunt… - Transportation …, 2023 - Elsevier
The resilient modulus (MR) of ballast is one of the key output parameters in any rail design
project because it controls the elastic magnitude of track deformation under cyclic loading …
project because it controls the elastic magnitude of track deformation under cyclic loading …
Selected AI optimization techniques and applications in geotechnical engineering
KC Onyelowe, FF Mojtahedi, AM Ebid… - Cogent …, 2023 - Taylor & Francis
In an age of depleting earth due to global warming impacting badly on the ozone layer of the
earth system, the need to employ technologies to substitute those engineering practices …
earth system, the need to employ technologies to substitute those engineering practices …
An evolutionary approach for modeling of shear strength of RC deep beams
AH Gandomi, GJ Yun, AH Alavi - Materials and structures, 2013 - Springer
In this study, a new variant of genetic programming, namely gene expression programming
(GEP) is utilized to predict the shear strength of reinforced concrete (RC) deep beams. A …
(GEP) is utilized to predict the shear strength of reinforced concrete (RC) deep beams. A …
Nonlinear genetic-based models for prediction of flow number of asphalt mixtures
AH Gandomi, AH Alavi, MR Mirzahosseini… - Journal of Materials in …, 2011 - ascelibrary.org
Rutting has been considered the most serious distress in flexible pavements for many years.
Flow number is an explanatory index for the evaluation of the rutting potential of asphalt …
Flow number is an explanatory index for the evaluation of the rutting potential of asphalt …
Insights into performance fitness and error metrics for machine learning
MZ Naser, A Alavi - arxiv preprint arxiv:2006.00887, 2020 - arxiv.org
Machine learning (ML) is the field of training machines to achieve high level of cognition and
perform human-like analysis. Since ML is a data-driven approach, it seemingly fits into our …
perform human-like analysis. Since ML is a data-driven approach, it seemingly fits into our …
Formulation of flow number of asphalt mixes using a hybrid computational method
AH Alavi, M Ameri, AH Gandomi… - … and Building Materials, 2011 - Elsevier
A high-precision model was derived to predict the flow number of dense asphalt mixtures
using a novel hybrid method coupling genetic programming and simulated annealing, called …
using a novel hybrid method coupling genetic programming and simulated annealing, called …