Cargando…

Unboxing machine learning models for concrete strength prediction using XAI

Concrete is a cost-effective construction material widely used in various building infrastructure projects. High-performance concrete, characterized by strength and durability, is crucial for structures that must withstand heavy loads and extreme weather conditions. Accurate prediction of concrete s...

Descripción completa

Detalles Bibliográficos
Autores principales:	Elhishi, Sara, Elashry, Asmaa Mohammed, El-Metwally, Sara
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2023
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10646149/ https://www.ncbi.nlm.nih.gov/pubmed/37963976 http://dx.doi.org/10.1038/s41598-023-47169-7

Ejemplares similares

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique
por: Adak, Anirban, et al.
Publicado: (2022)

Unboxing cluster heatmaps
por: Engle, Sophie, et al.
Publicado: (2017)

Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP
por: GhoshRoy, Debasmita, et al.
Publicado: (2023)

Unboxing the molecular modalities of mutagens in cancer
por: Kumari, Smita, et al.
Publicado: (2021)

Unboxing mathematics: creating a culture of modeling as critic
por: Gibbs, Antonnette M., et al.
Publicado: (2022)

Machine Learning Prediction Models to Evaluate the Strength of Recycled Aggregate Concrete
por: Yuan, Xiongzhou, et al.
Publicado: (2022)

High-Performance Concrete Strength Prediction Based on Machine Learning
por: Liu, Yanning
Publicado: (2022)

Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms
por: Ahmad, Ayaz, et al.
Publicado: (2021)

Prediction of Compressive Strength of Partially Saturated Concrete Using Machine Learning Methods
por: Candelaria, Ma. Doreen Esplana, et al.
Publicado: (2022)

Splitting tensile strength prediction of Metakaolin concrete using machine learning techniques
por: Li, Qiang, et al.
Publicado: (2023)

On the Use of Machine Learning Models for Prediction of Compressive Strength of Concrete: Influence of Dimensionality Reduction on the Model Performance
por: Wan, Zhi, et al.
Publicado: (2021)

Automated machine learning and explainable AI (AutoML-XAI) for metabolomics: improving cancer diagnostics
por: Bifarin, Olatomiwa O., et al.
Publicado: (2023)

Hackathons unboxed: a field guide to ideating, leading and winning
por: Chia, Alvin
Publicado: (2017)

Unboxing IT: a look inside the information technology black box
por: McCay, Christopher
Publicado: (2016)

Comprehensive Machine Learning-Based Model for Predicting Compressive Strength of Ready-Mix Concrete
por: Xu, Jiajia, et al.
Publicado: (2021)

Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete
por: Cao, Rongchuan, et al.
Publicado: (2022)

Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning
por: Kovačević, Miljan, et al.
Publicado: (2021)

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning
por: Shen, Zhongjie, et al.
Publicado: (2022)

Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete
por: Khan, Kaffayatullah, et al.
Publicado: (2022)

Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete
por: Ali, Ammar, et al.
Publicado: (2023)

Unboxing Android USB : a hands-on approach with real world examples
por: Regupathy, Rajaram
Publicado: (2014)

Hands-On Explainable AI (XAI) with Python
por: Rothman, Denis
Publicado: (2020)

Prediction of Compressive Strength of Sustainable Foam Concrete Using Individual and Ensemble Machine Learning Approaches
por: Ullah, Haji Sami, et al.
Publicado: (2022)

An XAI approach for COVID-19 detection using transfer learning with X-ray images
por: Sarp, Salih, et al.
Publicado: (2023)

Machine learning models development for shear strength prediction of reinforced concrete beam: a comparative study
por: Yaseen, Zaher Mundher
Publicado: (2023)

Unboxing the T‐box riboswitches—A glimpse into multivalent and multimodal RNA–RNA interactions
por: Zhang, Jinwei
Publicado: (2020)

Non-Tuned Machine Learning Approach for Predicting the Compressive Strength of High-Performance Concrete
por: Al-Shamiri, Abobakr Khalil, et al.
Publicado: (2020)

Concrete Strength Prediction Using Different Machine Learning Processes: Effect of Slag, Fly Ash and Superplasticizer
por: Qi, Chongchong, et al.
Publicado: (2022)

Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach
por: Moradi, Nozar, et al.
Publicado: (2022)

Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete
por: Pakzad, Seyed Soroush, et al.
Publicado: (2023)

Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification
por: Ghnemat, Rawan, et al.
Publicado: (2023)

Predictive Modelling for Concrete Failure at Anchorages Using Machine Learning Techniques
por: Spyridis, Panagiotis, et al.
Publicado: (2020)

Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI
por: Bhandari, Mohan, et al.
Publicado: (2022)

Advanced Machine Learning Modeling Approach for Prediction of Compressive Strength of FRP Confined Concrete Using Multiphysics Genetic Expression Programming
por: Ilyas, Israr, et al.
Publicado: (2022)

Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature
por: Ahmad, Ayaz, et al.
Publicado: (2021)

Application of Machine Learning Techniques for Predicting Compressive, Splitting Tensile, and Flexural Strengths of Concrete with Metakaolin
por: Shah, Hammad Ahmed, et al.
Publicado: (2022)

Research on prediction of compressive strength of fly ash and slag mixed concrete based on machine learning
por: Wang, Meng, et al.
Publicado: (2022)

Physics-assisted machine learning methods for predicting the splitting tensile strength of recycled aggregate concrete
por: Liu, Jianguo, et al.
Publicado: (2023)

Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study
por: Silva, Vitor Pereira, et al.
Publicado: (2023)

Machine Learning Techniques for Evaluating Concrete Strength with Waste Marble Powder
por: Sharma, Nitisha, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_2ro5ellsp3lkj1vmfu9omba665