35 Years of (AI) in geotechnical engineering: state of the art
AM Ebid - Geotechnical and Geological Engineering, 2021 - Springer
It was 35 years ago since the first usage of Artificial Intelligence (AI) technique in
geotechnical engineering, during those years many (AI) techniques were developed based …
geotechnical engineering, during those years many (AI) techniques were developed based …
Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications
W Fan, Y Chen, J Li, Y Sun, J Feng, H Hassanin… - Structures, 2021 - Elsevier
Abstract Machine learning is one of the key pillars of industry 4.0 that has enabled rapid
technological advancement through establishing complex connections among …
technological advancement through establishing complex connections among …
Forward forecast of stock price using sliding-window metaheuristic-optimized machine-learning regression
Time series forecasting has been widely used to determine the future prices of stock, and the
analysis and modeling of finance time series importantly guide investors' decisions and …
analysis and modeling of finance time series importantly guide investors' decisions and …
Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings
The building sector is the largest energy consumer accounting for 40% of global energy
usage. An energy forecast model supports decision-makers to manage electric utility …
usage. An energy forecast model supports decision-makers to manage electric utility …
[HTML][HTML] Improved prediction of clay soil expansion using machine learning algorithms and meta-heuristic dichotomous ensemble classifiers
Soil swelling-related disaster is considered as one of the most devastating geo-hazards in
modern history. Hence, proper determination of a soil's ability to expand is very vital for …
modern history. Hence, proper determination of a soil's ability to expand is very vital for …
Splitting tensile strength of cement soil reinforced with basalt fibers
S Wang, F Chen, Q Xue, P Zhang - Materials, 2020 - mdpi.com
Due to low splitting tensile strength, cement soil is more likely to experience dry shrinkage
and cracking in practical engineering. In this study, the mixing procedure of the cement soil …
and cracking in practical engineering. In this study, the mixing procedure of the cement soil …
Hybrid machine learning for predicting strength of sustainable concrete
Foamed concrete material is a sustainable material which is widely used in the construction
industry due to their sustainability. Accurate prediction of their compressive strength is vital …
industry due to their sustainability. Accurate prediction of their compressive strength is vital …
An advanced meta-learner based on artificial electric field algorithm optimized stacking ensemble techniques for enhancing prediction accuracy of soil shear strength
Shear strength is a crucial property of soils regarded as its intrinsic capacity to resist failure
when forces act on the soil mass. This study proposes an advanced meta-leaner to discern …
when forces act on the soil mass. This study proposes an advanced meta-leaner to discern …
[HTML][HTML] Exploring the effect of basalt fibers on maximum deviator stress and failure deformation of silty soils using ANN, SVM and FL supported by experimental data
Because the experimental trials in civil engineering field are difficult and time-consuming,
the application of artificial intelligence (AI) techniques is attracting considerable attention …
the application of artificial intelligence (AI) techniques is attracting considerable attention …
A novel hybrid model of augmented grey wolf optimizer and artificial neural network for predicting shear strength of soil
Due to the critical importance of accurate determination of soil shear strength (SSS) in major
civil engineering projects; this work is devoted to proposing hybrid models for estimating …
civil engineering projects; this work is devoted to proposing hybrid models for estimating …