Cargando…

Modelling the Influence of Waste Rubber on Compressive Strength of Concrete by Artificial Neural Networks

One of the major causes of ecological and environmental problems comes from the enormous number of discarded waste tires, which is directly connected to the exponential growth of the world’s population. In this paper, previous works carried out on the effects of partial or full replacement of aggreg...

Descripción completa

Detalles Bibliográficos
Autores principales:	Hadzima-Nyarko, Marijana, Nyarko, Emmanuel Karlo, Ademović, Naida, Miličević, Ivana, Kalman Šipoš, Tanja
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2019
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6416612/ https://www.ncbi.nlm.nih.gov/pubmed/30781883 http://dx.doi.org/10.3390/ma12040561

Ejemplares similares

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

Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self-Compacting Concrete with Class F Fly Ash
por: Kovačević, Miljan, et al.
Publicado: (2022)

Modelling daily water temperature from air temperature for the Missouri River
por: Zhu, Senlin, et al.
Publicado: (2018)

Recycled Rubber as an Aggregate Replacement in Self-Compacting Concrete—Literature Overview
por: Bušić, Robert, et al.
Publicado: (2018)

Assessing the performance of a suite of machine learning models for daily river water temperature prediction
por: Zhu, Senlin, et al.
Publicado: (2019)

Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder
por: Czarnecki, Sławomir, et al.
Publicado: (2022)

The Effect of Basalt Aggregates and Mineral Admixtures on the Mechanical Properties of Concrete Exposed to Sulphate Attacks
por: Karasin, Abdulhalim, et al.
Publicado: (2022)

Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network
por: Amar, Mouhamadou, et al.
Publicado: (2022)

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams
por: Kumar, Aman, et al.
Publicado: (2023)

Experimental Study of Rubberized Concrete Stress-Strain Behavior for Improving Constitutive Models
por: Strukar, Kristina, et al.
Publicado: (2018)

Compressive Strength Prediction of Rubber Concrete Based on Artificial Neural Network Model with Hybrid Particle Swarm Optimization Algorithm
por: Huang, Xiao-Yu, et al.
Publicado: (2022)

Application of Shape Memory Alloys in Retrofitting of Masonry and Heritage Structures Based on Their Vulnerability Revealed in the Bam 2003 Earthquake
por: Tabrizikahou, Alireza, et al.
Publicado: (2021)

A Brief Overview on Crack Patterns, Repair and Strengthening of Historical Masonry Structures
por: Latifi, Reza, et al.
Publicado: (2023)

Nano-Clay Platelet Integration for Enhanced Bending Performance of Concrete Beams Resting on Elastic Foundation: An Analytical Investigation
por: Chatbi, Mohammed, et al.
Publicado: (2023)

Analyzing the Compressive Strength of Ceramic Waste-Based Concrete Using Experiment and Artificial Neural Network (ANN) Approach
por: Song, Hongwei, et al.
Publicado: (2021)

An Analytical Mini-Review on the Compression Strength of Rubberized Concrete as a Function of the Amount of Recycled Tires Crumb Rubber
por: Lavagna, Luca, et al.
Publicado: (2020)

Numerical Simulation of the Effect of Freeze-Thaw Cycles on the Axial Compression Strength of Rubber Concrete
por: Hao, Dingyi, et al.
Publicado: (2023)

Influence of Basalt/Polypropylene Fiber on Permeability and Uniaxial Compressive Properties of Waste Tire Rubberized Concrete
por: Su, Dan-yang, et al.
Publicado: (2023)

Prediction of the Compressive Strength of Recycled Aggregate Concrete Based on Artificial Neural Network
por: Bu, Liangtao, et al.
Publicado: (2021)

Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete
por: Dao, Dong Van, et al.
Publicado: (2019)

Prediction Models for the Mechanical Properties of Self-Compacting Concrete with Recycled Rubber and Silica Fume
por: Bušić, Robert, et al.
Publicado: (2020)

2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System
por: Filko, Damir, et al.
Publicado: (2023)

Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models
por: Han, In-Ji, et al.
Publicado: (2019)

Automatic Robot-Driven 3D Reconstruction System for Chronic Wounds
por: Filko, Damir, et al.
Publicado: (2021)

Modeling the Thermoelastic Bending of Ferric Oxide (Fe(2)O(3)) Nanoparticles-Enhanced RC Slabs
por: Harrat, Zouaoui R., et al.
Publicado: (2023)

Overview of Concrete Performance Made with Waste Rubber Tires: A Step toward Sustainable Concrete
por: Ahmad, Jawad, et al.
Publicado: (2022)

Transition Zone Enhancement with Waste Limestone Powder as a Reason for Concrete Compressive Strength Increase
por: Kępniak, Maja, et al.
Publicado: (2021)

Recycled Untreated Rubber Waste for Controlling the Alkali–Silica Reaction in Concrete
por: Abbas, Safeer, et al.
Publicado: (2022)

Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model
por: Li, Chuanqi, et al.
Publicado: (2023)

Estimating Compressive Strength of Concrete Containing Untreated Coal Waste Aggregates Using Ultrasonic Pulse Velocity
por: Karimaei, Mahmood, et al.
Publicado: (2021)

Influence of Inertia on the Dynamic Compressive Strength of Concrete
por: Qin, Zhangchen, et al.
Publicado: (2022)

Bearing Strength of Crumb Rubber Concrete under Partial Area Loading
por: Xu, Xiaoqing, et al.
Publicado: (2020)

A Study of the Compressive Behavior of Recycled Rubber Concrete Reinforced with Hybrid Fibers
por: Li, Xiaohui, et al.
Publicado: (2023)

Systematic Experimental Assessment of POFA Concrete Incorporating Waste Tire Rubber Aggregate
por: Mhaya, Akram M., et al.
Publicado: (2022)

Thermal and Mechanical Properties of Concrete Incorporating Silica Fume and Waste Rubber Powder
por: Lakhiar, Muhammad Tahir, et al.
Publicado: (2022)

Estimating Compressive Strength of Concrete Using Neural Electromagnetic Field Optimization
por: Akbarzadeh, Mohammad Reza, et al.
Publicado: (2023)

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

Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models
por: Khan, Kaffayatullah, et al.
Publicado: (2022)

Experimental Investigation of the Stress–Strain Behavior and Strength Characterization of Rubberized Reinforced Concrete
por: Ullah, Hanif, et al.
Publicado: (2022)

Investigation of Strength Properties for Concrete Containing Fine-Rubber Particles Using UPV
por: Choi, Yeol, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_j40m57ne655igvfrbnf109326f