An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 5 | Issue - 4 | december 2023
Published
05 February, 2024
The evolution of concrete strength prediction methodologies has transitioned from empirical formulas based on experimental data to contemporary soft computing approaches. Initially, the concrete mix design was reliant on simple relationships between concrete mix proportions and compressive strength; later, the early techniques evolved to include statistical models incorporating material properties, curing conditions, and environmental variables. The advent of computational tools and artificial intelligence marked a paradigm shift, with accurate concrete strength prediction crucial for influencing structural integrity, safety, and cost-effectiveness in construction. The article explores empirical and analytical concrete strength prediction models before reviewing the application of soft computing approaches such as fuzzy logic, genetic algorithms, and neural networks. The integration of these models and hybrid approaches is discussed in this research study by highlighting their effectiveness in handling complex relationships within concrete mix parameters. A comparative analysis of various soft computing methods applied to structural and non-structural elements is carried out in this study to demonstrate their diverse applications and advantages in optimizing concrete mix designs, enhancing structural performance, and contributing to cost and time efficiency in construction processes.
KeywordsConcrete Strength Prediction Data Extraction Hybrid Approaches Soft Computing Applications
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