Predicting and controlling the ground vibration using gene expression programming (GEP) and teaching–learning-based optimization (TLBO) algorithms

Ground vibration is one of the most significant issues resulting from the blasting operation. This paper presents two empirical relationships based on gene expression programming (GEP) and teaching–learning-based optimization (TLBO) algorithms for predicting blast-induced peak particle velocity (PPV...

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Veröffentlicht in:	Environmental earth sciences Jg. 80; H. 22; S. 740
Hauptverfasser:	Dehghani, Hesam, Jodeiri Shokri, Behshad, Mohammadzadeh, Hoshiar, Shamsi, Reza, Abbas Salimi, Nasrin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2021 Springer Nature B.V
Schlagworte:	Algorithms Biogeosciences Blasting Comparative analysis Correlation coefficient Correlation coefficients Curve fitting Earth and Environmental Science Earth Sciences Empirical analysis Environmental Science and Engineering Gene expression Geochemistry Geology Ground motion Hydrology/Water Resources Iran Machine learning Optimization Orbital velocity Original Article Parameters Predictive control Root-mean-square errors Statistical methods Statistics Terrestrial Pollution Vibration villages Iran Peak particle velocity (PPV) Gene expression programming (GEP) Teaching–learning-based optimization (TLBO) Blasting
ISSN:	1866-6280, 1866-6299
Online-Zugang:	Volltext
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