I hold a PhD in civil engineering and work on machine learning, explainable AI, and optimisation for structures and infrastructure. Most of my research is about predicting the strength, failure, and durability of concrete and steel members from data, and about finding the causes behind infrastructure failures. I have also worked on geotechnical stability, pipe-fluid systems, structural performance under fire, and low-carbon concrete design. In these studies, I mainly use ensemble learning, SHAP and LIME for interpretation, symbolic regression, causal discovery and inference, generative models that produce synthetic data when experiments are limited, and metaheuristic optimisation. My work has received more than 780 citations, and my h-index is 14.
Topic 3. Smart, Secure and Adaptive Water Infrastructures for Sustainable Water Management
I am a civil engineer working on machine learning and explainable, causal AI for structures. Water infrastructure is built from concrete, steel, and geotechnical assets, and in practice their safety has to be judged from very few measurements. That is the problem I work on. I have used causal inference to study bridge failures from observational data, and I have also worked on slope stability in disturbed rock masses, the dynamic behaviour of pipe-fluid systems, offshore structures, and the loss of bond strength in reinforced concrete under corrosion. When test data are scarce, I generate synthetic data with generative models. I also keep the models interpretable, using SHAP, LIME, and symbolic regression, so that an engineer can see what drives a prediction instead of trusting a black box.
In a consortium, I could take on the condition and risk assessment of dams, tanks, pipelines, and treatment plants. Another area I can contribute to is low-carbon design, since I recently worked on the carbon footprint of concrete mixtures.
machine learning, explainable ai, causal inference, infrastructure resilience, structural condition assessment, dam safety, water pipelines, concrete durability, risk assessment, optimisation
I have been part of SEMIT, the International Conference on Science, Engineering Management and Information Technology, since 2022, and of ODSIE, the International Conference on Optimization and Data Science in Industrial Engineering. I serve on the scientific committee of both conferences and chair panels there every year, so I am in regular contact with researchers working on optimisation, data science and their engineering applications. I plan to attend the next editions of both and would be g