An article by Manuel Parente, Head of AI in BUILT CoLAB, with the title “Predictive and prescriptive analytics in transportation geotechnics: Three case studies” has just been accepted and published online in open format.
This article, which will be edited in volume 5 of Elsevier’s Transportation Engineering magazine in September 2021, has the following research topics:
• Artificial intelligence (AI) can support Transformation Infrastructure Management (TIM);
• TIM predictive analytics can be obtained by using Machine Learning (ML);
• TIM prescriptive analytics can be obtained by using Evolutionary Computation (EC);
• Quality Neural Networks and Support Vector Machines predictions were obtained;
• Complex ML models can be opened by using eXplainable AI (XAI) methods.
«Transportation infrastructure is of paramount importance for any country. The construction, management and maintenance of this infrastructure is a complex task that requires a significant amount of resources (e.g., human work equipment, materials, maintenance costs). To better support this task, in the last decades several Artificial Intelligence (AI) data analysis tools have been proposed. In this paper, we summarize recent predictive and prescriptive AI applications to the transportation infrastructure field, underlying their strategic impact. In particular, we discuss three case studies: the design of better earthwork projects; the prediction of jet grouting soilcrete mechanical and physical properties (uniaxial compressive strength, stiffness and column diameter); and prediction of the stability level of engineered slopes.»
The article was written in co-authorship with Joaquim Tinoco, António Gomes Correia, Paulo Cortez and David Toll, and is available for consultation on the Science Direct portal here.