Neural Network - Concrete Strength
(Adam Optimizer)

Trained - Range ( 26.06 to 82.06 MPa )

This project involves development of a neural network model to predict the compressive strength of concrete using a dataset sourced from the Machine Learning Repository of the University of California, Irvine (UCI). The dataset, created by I-Cheng Yeh, provides valuable insights into how different components and factors contribute to the compressive strength of concrete.

Dataset Overview:

• Source: (For validation , click the following link) https://archive.ics.uci.edu/dataset/165/concrete+compressive+strength ( UCI Machine learning Repo )
• Creator: I-Cheng Yeh
• Description: The dataset consists of 1,030 instances with 8 input variables (features) and one target output (compressive strength).

Input Variables for 1 cubic meter of concrete:

Material	Unit	Data Type
Cement	kg	Continuous
Blast Furnace Slag	kg	Integer
Fly Ash	kg	Continuous
Water	kg	Continuous
Superplasticizer	kg	Continuous
Coarse Aggregate	kg	Continuous
Fine Aggregate	kg	Continuous
Age	days	Continuous

All features are measured in a kg per cubic meter of concrete except for Age, which is measured in days.

Project Description:

This project utilizes deep learning to predict concrete compressive strength, focusing on the relationships between various input features and the target outcome. The neural network was developed using the UCI dataset, chosen for its comprehensive real-world data on concrete mixtures. By training with 25,000 epochs and using an architecture of over 500 neurons, the model achieves high accuracy in predicting concrete strength based on its composition and age.

The neural network was trained using a dataset with 25,000 epochs to ensure thorough learning and convergence. The network architecture was robust, featuring over 500 neurons to capture the complexity of the input-output relationships.The neural network functions by passing the input data through matrix neurons, followed by a ReLU activation function after each layer. This choice of activation function helps introduce non-linearity into the model, enabling it to learn complex patterns in the data effectively.

The coefficient of determination (R²) value is 0.9466, indicating the reliability of the model's predictions in accurately aligning with the actual results

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Please enter the quantity of each material in kilograms per cubic meter of concrete (kg/m³) except Age which is in days. Please ensure the value is within the placeholder's range

Calculate

Result:

Predicted Strength (MPa):