Concrete Compressive Strength Prediction Using DL and AutoML

Concrete is one of the most widely used construction materials in civil engineering. The compressive strength of concrete is a key factor that determines the durability and safety of structures such as buildings, bridges, and roads. However, predicting the compressive strength of concrete is a complex task because it depends on several factors including the proportions of ingredients and curing age.

This project focuses on predicting the compressive strength of concrete using deep learning techniques and Automated Machine Learning (AutoML). The dataset used in this project contains various ingredients of concrete such as cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age.

Data preprocessing techniques such as handling missing values, normalization, and feature selection are applied before training the models. A deep learning model is developed using neural networks to learn the nonlinear relationships between the input ingredients and the compressive strength of concrete. Additionally, AutoML techniques are used to automatically select and optimize machine learning models.

The final trained model can predict the compressive strength of concrete based on the given input parameters. This project demonstrates the application of artificial intelligence in civil engineering for improving construction quality and decision-making.
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https://projectmentor.pro/product/concrete-compressive-strength-prediction-using-deep-learning-and-automl-QKXUi0
#DeepLearning
#MachineLearning
#AutoML
#PredictiveAnalytics
#CivilEngineering
#DataScience

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