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 …
Machine learning to inform tunnelling operations: Recent advances and future trends
BB Sheil, SK Suryasentana… - Proceedings of the …, 2020 - icevirtuallibrary.com
The proliferation of data collected by modern tunnel-boring machines (TBMs) presents a
substantial opportunity for the application of machine learning (ML) to support the decision …
substantial opportunity for the application of machine learning (ML) to support the decision …
Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models
This study aims to implement a hybrid ensemble surrogate machine learning technique in
predicting the compressive strength (CS) of concrete, an important parameter used for …
predicting the compressive strength (CS) of concrete, an important parameter used for …
Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques
In this study, a model for the estimation of the compressive strength of concretes
incorporating metakaolin is developed and parametrically evaluated, using soft computing …
incorporating metakaolin is developed and parametrically evaluated, using soft computing …
Machine learning models for predicting compressive strength of fiber-reinforced concrete containing waste rubber and recycled aggregate
The compressive strength of fiber-reinforced rubberized recycled aggregate concrete (FR 3
C) is an important performance indicator for its practical application and durability in the …
C) is an important performance indicator for its practical application and durability in the …
Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models
The present study aims to compare the performance of two machine learning techniques
that can unveil the relationship between the input and target variables and predict the …
that can unveil the relationship between the input and target variables and predict the …
Prediction of heating and cooling loads based on light gradient boosting machine algorithms
J Guo, S Yun, Y Meng, N He, D Ye, Z Zhao, L Jia… - Building and …, 2023 - Elsevier
Abstract Machine learning models have been widely used to study the prediction of heating
and cooling loads in residential buildings. However, most of these methods use the default …
and cooling loads in residential buildings. However, most of these methods use the default …
Soft computing-based models for the prediction of masonry compressive strength
Masonry is a building material that has been used in the last 10.000 years and remains
competitive today for the building industry. The compressive strength of masonry is used in …
competitive today for the building industry. The compressive strength of masonry is used in …
Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks
This paper reports the results of soft computing-based models correlating L and N-type
Schmidt hammer rebound numbers of rock. A data-independent database was compiled …
Schmidt hammer rebound numbers of rock. A data-independent database was compiled …
A novel feature selection approach based on tree models for evaluating the punching shear capacity of steel fiber-reinforced concrete flat slabs
When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is very important
to predict their punching shear capacity accurately. The use of machine learning seems to …
to predict their punching shear capacity accurately. The use of machine learning seems to …