Seismic response prediction of a train-bridge coupled system based on a LSTM neural network

P Xiang, P Zhang, H Zhao, Z Shao… - Mechanics Based Design …, 2024 - Taylor & Francis
High complexity and randomness in high-speed train-bridge interactive dynamic analysis
under earthquake lead to massive calculations in high-speed railway seismic design. To …

A novel hunger games search optimization-based artificial neural network for predicting ground vibration intensity induced by mine blasting

H Nguyen, XN Bui - Natural Resources Research, 2021 - Springer
Innovation efforts in developing soft computing models (SCMs) of researchers and scholars
are significant in recent years, especially for problems in the mining industry. So far, many …

Predicting clay compressibility using a novel Manta ray foraging optimization-based extreme learning machine model

PG Asteris, A Mamou, M Ferentinou, TT Tran… - Transportation …, 2022 - Elsevier
Designing infrastructure founded on very soft deposits requires soil improvement to reduce
the compressibility of clay so as to prevent the development of unacceptably high differential …

Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms

H Zhang, H Nguyen, XN Bui, B Pradhan, NL Mai… - Resources Policy, 2021 - Elsevier
The focus of this study aims at developing two novel hybrid intelligence models for
forecasting copper prices in the future with high accuracy based on the extreme learning …

A new long-term photovoltaic power forecasting model based on stacking generalization methodology

E Ofori-Ntow Jnr, YY Ziggah, MJ Rodrigues… - Natural Resources …, 2022 - Springer
In recent times, solar energy has become a highly promising source of energy and one of
the most regular types of sustainable energy. Forecasting the availability of solar energy has …

A mathematical-mechanical hybrid driven approach for determining the deformation monitoring indexes of concrete dam

K Zhang, C Gu, Y Zhu, Y Li, X Shu - Engineering Structures, 2023 - Elsevier
This paper aims at quantifying the uncertainty of mechanical parameters of the concrete dam
based on prototype measured data, and puts forward a novel method for determining …

Enhancing predictions of blast-induced ground vibration in open-pit mines: Comparing swarm-based optimization algorithms to optimize self-organizing neural …

H Nguyen, XN Bui, E Topal - International Journal of Coal Geology, 2023 - Elsevier
The objective of this paper is to present a method for predicting blast-induced ground
vibration in open-pit mines that is based on the use of self-organizing neural networks …

Support vector regression optimized by black widow optimization algorithm combining with feature selection by MARS for mining blast vibration prediction

G Xu, X Wang - Measurement, 2023 - Elsevier
Ground vibration induced by mine blasting is the most significant adverse effect on nearby
residents and surroundings. Accurate prediction of blasting vibration using limited monitor …

[HTML][HTML] Assessing ground vibration caused by rock blasting in surface mines using machine-learning approaches: A comparison of CART, SVR and MARS

GC Komadja, A Rana, LA Glodji, V Anye, G Jadaun… - Sustainability, 2022 - mdpi.com
Ground vibration induced by rock blasting is an unavoidable effect that may generate severe
damages to structures and living communities. Peak particle velocity (PPV) is the key …

[PDF][PDF] Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models.

QH Tran, H Nguyen, XN Bui - CMES-Computer Modeling in …, 2023 - cdn.techscience.cn
This study considered and predicted blast-induced ground vibration (PPV) in open-pit mines
using bagging and sibling techniques under the rigorous combination of machine learning …