ArticleMaterials (Basel, Switzerland)2023
Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material.
Article in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 27 citations in OpenAlex.
- Advanced Computational Methods for Modeling, Prediction and Optimization-A Review.Materials (Basel, Switzerland) · 2024Review
- A Rolling Bearing Fault Feature Extraction Algorithm Based on IPOA-VMD and MOMEDA.Sensors (Basel, Switzerland) · 2023Article
- Metaheuristic Optimization of Random Forest for Predicting Punch Shear Strength of FRP-Reinforced Concrete Beams.Materials (Basel, Switzerland) · 2023Article
- Study on the Mechanical Behavior of a Dual-Density Hybrid Lattice Structure under Quasi-Static and Dynamic Compressions.Materials (Basel, Switzerland) · 2023Article
- Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model.Materials (Basel, Switzerland) · 2023Article
- Municipal Sewage Sludge as a Source for Obtaining Efficient Biosorbents: Analysis of Pyrolysis Products and Adsorption Tests.Materials (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
The application of aseismic materials in foundation engineering structures is an inevitable trend and research hotspot of earthquake resistance, especially in tunnel engineering. In this study, the pelican optimization algorithm (POA) is improved using the Latin hypercube sampling (LHS) method and the Chaotic mapping (CM) method to optimize the random forest (RF) model for predicting the aseismic performance of a novel aseismic rubber-concrete material. Seventy uniaxial compression tests and seventy impact tests were conducted to quantify this aseismic material performance, i.e., strength and energy absorption properties and four other artificial intelligence models were generated to compare the predictive performance with the proposed hybrid RF models. The performance evaluation results showed that the LHSPOA-RF model has the best prediction performance among all the models for predicting the strength and energy absorption property of this novel aseismic concrete material in both the training and testing phases (R
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Registered trials
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