Having Deep Investigation on Predicting Unconfined Compressive Strength by Decision Tree in Hybrid and Individual Approaches

In the field of geotechnical engineering Rocks' unconfined compressive strength (UCS) is an important variable that plays a significant part in civil engineering projects like foundation design, mining, and tunneling. These projects' stability and safety depend on how accurately UCS predic...

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Bibliographic Details
Published in:International journal of advanced computer science & applications Vol. 15; no. 7
Main Authors: Zhang, Qingqing, Wang, Lei, Gu, Hongmei
Format: Journal Article
Language:English
Published: West Yorkshire Science and Information (SAI) Organization Limited 2024
Subjects:
Accuracy
Algorithms
Compressive strength
Decision trees
Geotechnical engineering
Heuristic methods
Machine learning
Performance measurement
Prediction models
Search algorithms
Soil analysis
Soil dispersion
Soil properties
Soil shrinkage
Soil stabilization
Soil testing
Stabilization
ISSN:2158-107X, 2156-5570
Online Access:Get full text
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Summary:In the field of geotechnical engineering Rocks' unconfined compressive strength (UCS) is an important variable that plays a significant part in civil engineering projects like foundation design, mining, and tunneling. These projects' stability and safety depend on how accurately UCS predicts the future. In this study, machine learning (ML) techniques are applied to forecast UCS for soil-stabilizer combinations. This study aims to build complex and highly accurate predictive models using the robust Decision Tree (DT) as a primary ML tool. These models show relationships between UCS considering a variety of intrinsic soil properties, including dispersion, plasticity, linear particle size shrinkage, and the kind of and number of stabilizing additives. Furthermore, this paper integrates two meta-heuristic algorithms: the Population-based‎ vortex search algorithm (PVS) and the Arithmetic optimizer algorithm (AOA) to enhance the precision of models. These algorithms work in tandem to bolster the accuracy of predictive models. This study has subjected models to rigorous validation by analyzing UCS samples from different soil types, drawing from historical stabilization test results. This study unveils three noteworthy models: DTAO, DTPB, and an independent DT model. Each model provides invaluable insights that support the meticulous projection of UCS for soil-stabilizer blends. Notably, the DTAO model stands out with exceptional performance metrics. With an R2 value of 0.998 and an impressively low RMSE of 1.242, it showcases precision and reliability. These findings not only underscore the accuracy of the DTAO model but also emphasize its effectiveness in predicting soil stabilization outcomes.
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2024.0150712