[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 …
[HTML][HTML] A novel explainable AI-based approach to estimate the natural period of vibration of masonry infill reinforced concrete frame structures using different machine …
P Thisovithan, H Aththanayake, DPP Meddage… - Results in …, 2023 - Elsevier
In this study, we used four different machine learning models-artificial neural network (ANN),
support vector regression (SVR), k-nearest neighbor (KNN), and random forest (RF)-to …
support vector regression (SVR), k-nearest neighbor (KNN), and random forest (RF)-to …
[HTML][HTML] Adapting cities to the surge: A comprehensive review of climate-induced urban flooding
Climate change is a serious global issue causing more extreme weather patterns, resulting
in more frequent and severe events like urban flooding. This review explores the connection …
in more frequent and severe events like urban flooding. This review explores the connection …
[HTML][HTML] Recent Applications of Explainable AI (XAI): A Systematic Literature Review
This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …
[HTML][HTML] Predictive modeling for compressive strength of 3D printed fiber-reinforced concrete using machine learning algorithms
Abstract Three-dimensional (3D) printing in the construction industry is growing rapidly due
to its inherent advantages, including intricate geometries, reduced waste, accelerated …
to its inherent advantages, including intricate geometries, reduced waste, accelerated …
Modeling streamflow in non-gauged watersheds with sparse data considering physiographic, dynamic climate, and anthropogenic factors using explainable soft …
Streamflow forecasting is essential for effective water resource planning and early warning
systems. Streamflow and related parameters are often characterized by uncertainties and …
systems. Streamflow and related parameters are often characterized by uncertainties and …
[HTML][HTML] A novel machine learning approach for diagnosing diabetes with a self-explainable interface
This study introduces the first-ever self-explanatory interface for diagnosing diabetes
patients using machine learning. We propose four classification models (Decision Tree (DT) …
patients using machine learning. We propose four classification models (Decision Tree (DT) …
[HTML][HTML] An explainable machine learning approach to predict the compressive strength of graphene oxide-based concrete
Graphene oxide (GO) has shown promise in improving concrete strength. Despite its
frequent use in cement composites, its effect on concrete properties is less explored. The …
frequent use in cement composites, its effect on concrete properties is less explored. The …
[HTML][HTML] A new frontier in streamflow modeling in ungauged basins with sparse data: A modified generative adversarial network with explainable AI
Streamflow forecasting is crucial for effective water resource planning and early warning
systems, especially in regions with complex hydrological behaviors and uncertainties. While …
systems, especially in regions with complex hydrological behaviors and uncertainties. While …
[HTML][HTML] Predicting transient wind loads on tall buildings in three-dimensional spatial coordinates using machine learning
Abstract Machine learning (ML) as a subset of artificial intelligence (AI), has gained
significant attention in wind engineering applications over the past decade. Wind load …
significant attention in wind engineering applications over the past decade. Wind load …