Concrete manufacturing projects are one of the most common ones for consulting agencies. Because of the highly non-linear dependency of input materials like ash, water, cement, superplastic, etc; with the resultant strength of concrete, it gets difficult for machine learning models to successfully capture this relation and perform cost optimizations. This paper highlights how PINNs (Physics Informed Neural Networks) can be useful in the given situation. This state-of-the-art model shall also get compared with traditional models like Linear Regression, Random Forest, Gradient Boosting, and Deep Neural Network. Results of the research highlights how well PINNs performed even with reduced dataset, thus resolving one of the biggest issues of limited data availability for ML models. On an average, PINN got the loss value reduced by 26.3% even with 40% lesser data compared to the Deep Neural Network. In addition to predicting strength of the concrete given the quantity of raw materials, the paper also highlights the use of heuristic optimization method like Particle Swarm Optimization (PSO) in predicting quantity of raw materials required to manufacture concrete of given strength with least cost.

In the last few years, Artificial Intelligence and Machine Learning have seen an unprecedented rise in popularity across industries and areas of scientific research. Businesses are looking for ways to integrate these new technologies into their operations. However, the shortage of qualified data scientist and machine learning experts has been one of the challenges which thwart the adoption of AI. But a growing number of tools are bringing these capabilities into the hands of developers, citizen data scientists, domain experts and business users. In this blog, we will delve into Automated Machine Learning (AutoML) for Data Flows – a new capability in Power BI which enables business users to experience machine learning models without having to learn how to program or extensive knowledge of mathematics and statistics. AutoML for dataflows allows users to create Machine Learning models with few simple clicks and generates model summary reports.

It is said to have the potential to save the industry countless hours and millions of pounds a year. The strength prediction engine was developed in collaboration with BAM Nuttall, using funding from an Innovate UK grant awarded last year. The system is already being used on BAM Nuttall's London City Airport expansion project. Development of the system was made possible by Converge's access to a huge data set on concrete performance, paving the way for the commercial application of machine learning to monitor and predict material performance in a live project. BAM Nuttall head of innovation Colin Evison said: "This advancement in construction technology is a game changer. The Converge prediction engine gives us insight into material performance we didn't think possible. We are delighted to be Converge's industry partner in bringing this exciting new tool to market."

In this post I will show how to apply neural network in a scenario in R and how to see the results and hidden layers in a plot. For this post I got some great example from [1]. Concert has been use in many different structure such as bridge, apartment, roadways and so on. For the safety the strength of the concrete is matter. In this example, we are going to predict a value, that is concrete strength.