Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites
Ongoing anthropogenic carbon dioxide (CO 2) emissions to the atmosphere cause severe
air pollution that leads to complex changes in the climate, which pose threats to human life …
air pollution that leads to complex changes in the climate, which pose threats to human life …
Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids
Careful design and preparation of drilling fluids with appropriate rheology and filtration
properties, combined with operational monitoring, is essential for successful drilling …
properties, combined with operational monitoring, is essential for successful drilling …
Machine learning approaches for prediction of the bearing capacity of ring foundations on rock masses
Determining the bearing capacity of ring foundations on rock masses holds utmost
importance within the framework of foundation design methodology. To examine the failure …
importance within the framework of foundation design methodology. To examine the failure …
An Explicit Model for Soil Resilient Modulus Incorporating Freezing–Thawing Cycles Through Offspring Selection Genetic Algorithm (OSGA)
Determining soil resilient modulus for pavement design traditionally involves resource-
intensive repeated load triaxial testing, prompting the need for a reliable and efficient …
intensive repeated load triaxial testing, prompting the need for a reliable and efficient …
Optimization of an Artificial Neural Network Using Three Novel Meta-heuristic Algorithms for Predicting the Shear Strength of Soil
Shear strength of soil (SSS) is crucial in civil engineering for foundations, highways, earth fill
dams, slope stability, airfields, and coastal structure design. Measuring SSS at a field scale …
dams, slope stability, airfields, and coastal structure design. Measuring SSS at a field scale …
Developing a New Procedural Binary Particle Swarm Optimization Algorithm to Estimate Some Properties of Local Concrete Mixtures
Artificial intelligence techniques have lately been used to estimate the mechanical
properties of concrete to reduce time and financial expenses, but these techniques differ in …
properties of concrete to reduce time and financial expenses, but these techniques differ in …
A case study of resilient modulus prediction leveraging an explainable metaheuristic-based XGBoost
The resilient modulus (MR) of pavement subgrade soils is an index describing the structural
response of flexible pavement foundations. Commonly, MR under different conditions of …
response of flexible pavement foundations. Commonly, MR under different conditions of …
Application of AI models for reliability assessment of 3d slope stability of a railway embankment
In this work, three machine learning (ML) techniques ie, multivariate adaptive regression
splines (MARS), support vector machine (SVM), and least square vector machine (LSSVM) …
splines (MARS), support vector machine (SVM), and least square vector machine (LSSVM) …
Short-term PV power prediction based on VMD-CNN-IPSO-LSSVM hybrid model
J Jiang, S Hu, L Xu, T Wang - International Journal of Low …, 2024 - academic.oup.com
This article discusses the significance and obstacles of short-term power prediction in
photovoltaic systems and introduces a hybrid model for photovoltaic short-term power …
photovoltaic systems and introduces a hybrid model for photovoltaic short-term power …
Real-time monitoring and optimization of drilling performance using artificial intelligence techniques: a review
DA Wood - Sustainable Natural Gas Drilling, 2024 - Elsevier
Brief summary Multiple supervised and unsupervised artificial intelligence techniques have
been adapted and applied for real-time drilling monitoring and optimization purposes. This …
been adapted and applied for real-time drilling monitoring and optimization purposes. This …