Closed-form equation for estimating unconfined compressive strength of granite from three non-destructive tests using soft computing models
The use of three artificial neural network (ANN)-based models for the prediction of
unconfined compressive strength (UCS) of granite using three non-destructive test …
unconfined compressive strength (UCS) of granite using three non-destructive test …
[HTML][HTML] Artificial intelligence in tunnel construction: A comprehensive review of hotspots and frontier topics
L Liu, Z Song, X Li - Geohazard Mechanics, 2024 - Elsevier
Abstract Application of Artificial Intelligence (AI) in tunnel construction has the potential to
transform the industry by improving efficiency, safety, and cost-effectiveness. This paper …
transform the industry by improving efficiency, safety, and cost-effectiveness. This paper …
[HTML][HTML] Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning
Geopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC
(Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With …
(Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With …
Design of concrete incorporating microencapsulated phase change materials for clean energy: A ternary machine learning approach based on generative adversarial …
The inclusion of microencapsulated phase change materials (MPCM) in construction
materials is a promising solution for increasing the energy efficiency of buildings and …
materials is a promising solution for increasing the energy efficiency of buildings and …
Optimized machine learning modelling for predicting the construction cost and duration of tunnelling projects
Predicting duration and cost of tunnelling projects is an essential factor in determining the
usefulness of a decision-making system. Therefore, research on the duration and cost of …
usefulness of a decision-making system. Therefore, research on the duration and cost of …
[PDF][PDF] Rock Strength Estimation Using Several Tree-Based ML Techniques.
The uniaxial compressive strength (UCS) of rock is an essential property of rock material in
different relevant applications, such as rock slope, tunnel construction, and foundation. It …
different relevant applications, such as rock slope, tunnel construction, and foundation. It …
Machine learning techniques to predict rock strength parameters
A Mahmoodzadeh, M Mohammadi… - Rock Mechanics and …, 2022 - Springer
To accurately estimate the rock shear strength parameters of cohesion (C) and friction angle
(φ), triaxial tests must be carried out at different stress levels so that a failure envelope can …
(φ), triaxial tests must be carried out at different stress levels so that a failure envelope can …
[HTML][HTML] Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using an explainable artificial intelligence
The durability of rocks is a substantial rock property that has to be considered for designing
geotechnical structures. Uniaxial compressive strength (UCS) and Young's modulus (E) are …
geotechnical structures. Uniaxial compressive strength (UCS) and Young's modulus (E) are …
Prediction of safety factors for slope stability: comparison of machine learning techniques
Because of the disasters associated with slope failure, the analysis and forecasting of slope
stability for geotechnical engineers are crucial. In this work, in order to forecast the factor of …
stability for geotechnical engineers are crucial. In this work, in order to forecast the factor of …
Presenting the best prediction model of water inflow into drill and blast tunnels among several machine learning techniques
During the construction of a tunnel, water inflow is one of the most common and complex
geological disasters and has a large impact on the construction schedule and safety. When …
geological disasters and has a large impact on the construction schedule and safety. When …