[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 …
Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface
This study investigated the importance of applying explainable artificial intelligence (XAI) on
different machine learning (ML) models developed to predict the strength characteristics of …
different machine learning (ML) models developed to predict the strength characteristics of …
[HTML][HTML] Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfer
The use of data-driven methods in fluid mechanics has surged dramatically in recent years
due to their capacity to adapt to the complex and multi-scale nature of turbulent flows, as …
due to their capacity to adapt to the complex and multi-scale nature of turbulent flows, as …
[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 …
Interpretable machine learning methods for clarification of load-displacement effects on cable-stayed bridge
Cable-stayed bridges play a crucial role in various transportation systems, facilitating the
movement of pedestrians, automobiles, and trains. Accurately estimating structural …
movement of pedestrians, automobiles, and trains. Accurately estimating structural …
[HTML][HTML] Evaluating expressway traffic crash severity by using logistic regression and explainable & supervised machine learning classifiers
The number of expressway road accidents in Sri Lanka has significantly increased (by 20%)
due to the expansion of the transport network and high traffic volume. It is crucial to identify …
due to the expansion of the transport network and high traffic volume. It is crucial to identify …
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) …
Interpretation of machine-learning-based (black-box) wind pressure predictions for low-rise gable-roofed buildings using Shapley additive explanations (SHAP)
Conventional methods of estimating pressure coefficients of buildings retain time and cost
constraints. Recently, machine learning (ML) has been successfully established to predict …
constraints. Recently, machine learning (ML) has been successfully established to predict …