Smart Engineering works on the development of tools based on autonomous learning
Smart Engineering has started a new project at the beginning of the year for the development of tools based on autonomous learning and massive data analysis, with application in different areas of civil engineering ranging from the precast sector to construction. This project is part of a Torres Quevedo grant for the incorporation of doctors to private companies, from the Ministry of Science and Innovation (PTQ-18-90877). Thanks to the aforementioned help, Tai Ikumi PhD has joined Smart Engineering.
Nowadays, data is positioned as one of the most dynamic sources of economic benefit in many economic sectors. Data analysis has shown that it has the ability to improve the performance and productivity of any economic activity. However, its use in the civil engineering sector still remains marginal. In fact, it is often openly recognized that in many cases the registered data is not even analyzed. This is due to the difficulty of allocating highly qualified human technical resources to tasks that are still carried out very manually today in the field of civil engineering. These types of practices run counter to current trends, as they disregard the enormous potential of the data.
The general objective of the project is the development of data-based products for the optimization of construction processes. To meet this technological and scientific challenge, models based on machine learning are currently being developed capable of predicting the evolution of the mechanical properties of different concretes (conventional, ultra-high resistance, sprayed, fibre-reinforced) based on dosage and curing conditions. This line of activity encompasses the existing project with Chatu Tech for the development and commercialization of chips with RFID technology for use in concrete structures and elements.
The integrated sensor solution together with the intelligent algorithm allows real-time wireless monitoring of the resistance evolution in concrete elements. This is a critical piece of information that enables the optimization of a multitude of construction processes, such as the formwork stripping time, the opening pavement to traffic, the tensioning time of the prestressed cable, etc. Any reduction in construction / manufacturing times translates directly into a minimization of the environmental impact of the activities and the associated costs. Currently, the first tests on an industrial scale are already being carried out, both in the field of civil works, underground works and precast, resulting in very promising results.