ArticleSensors (Basel, Switzerland)2017
Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- An EPR model for predicting the bearing capacity of single and double-strip foundations near earth slope crests.PloS one · 2024Article
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- Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data.Sensors (Basel, Switzerland) · 2022Article
- Prediction of the Compressive Strength of Recycled Aggregate Concrete Based on Artificial Neural Network.Materials (Basel, Switzerland) · 2021Article
- Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms.Materials (Basel, Switzerland) · 2021Article
- Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction.Sensors (Basel, Switzerland) · 2021Article
- A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering.Sensors (Basel, Switzerland) · 2021Review
- Article
- A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs.Materials (Basel, Switzerland) · 2020Article
- Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression.Materials (Basel, Switzerland) · 2020Article
- Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete.Materials (Basel, Switzerland) · 2020Article
- Soft Sensors in the Primary Aluminum Production Process Based on Neural Networks Using Clustering Methods.Sensors (Basel, Switzerland) · 2019Article
- The Nature-Inspired Metaheuristic Method for Predicting the Creep Strain of Green Concrete Containing Ground Granulated Blast Furnace Slag.Materials (Basel, Switzerland) · 2019Article
- A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces.Sensors (Basel, Switzerland) · 2017Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
This work presents a soft-sensor approach for estimating critical mechanical properties of sandcrete materials. Feed-forward (FF) artificial neural network (ANN) models are employed for building soft-sensors able to predict the 28-day compressive strength and the modulus of elasticity of sandcrete materials. To this end, a new normalization technique for the pre-processing of data is proposed. The comparison of the derived results with the available experimental data demonstrates the capability of FF ANNs to predict with pinpoint accuracy the mechanical properties of sandcrete materials. Furthermore, the proposed normalization technique has been proven effective and robust compared to other normalization techniques available in the literature.
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