Structural Engineering · AI Research

Toward more resilient structures.

Petros Lazaridis is a civil engineer and doctoral researcher exploring how nonlinear structural analysis and interpretable machine learning can help us understand and predict seismic damage.

Explore research About Petros
Democritus University of Thrace
Laboratory of Statics and Dynamics of Structures
Research themesEarthquake EngineeringNonlinear AnalysisExplainable Machine Learning
01 / Areas of inquiry

Where structural mechanics meets data.

Research focused on understanding structural response, assessing seismic damage, and making predictive tools more transparent and useful.

01

Seismic damage prediction

Estimating the damage state of buildings after earthquake excitation using structural response indicators and data-driven models.

02

Earthquake sequences

Investigating how repeated seismic events and accumulated damage influence structural vulnerability and performance.

03

Explainable artificial intelligence

Connecting machine-learning predictions with engineering understanding through interpretable features and model behaviour.

02 / Research approach

From physical behaviour to useful predictions.

A combined computational and data-driven perspective, with engineering meaning at the centre of the analysis.

01
Understand structural responseNonlinear modelling and dynamic analysis of civil engineering structures.
02
Learn from response dataUse of carefully selected intensity and response measures in predictive models.
03
Interpret and communicateExplanations, limitations, and insights that can inform engineering judgement.
03 / About

Petros Lazaridis

Civil Engineer, MSc, and PhD candidate at Democritus University of Thrace. His research interests lie in earthquake engineering, damage assessment, nonlinear structural response, and applications of artificial intelligence in structural engineering.

Field
Civil & Structural Engineering
Institution
Democritus University of Thrace
Focus
Seismic damage, NLTHA, and interpretable ML
More interpretable predictions. More informed structural decisions.
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