Applications of artificial intelligence for explainability and uncertainty quantification for performance-based design and damage classification in civil engineering

Thumbnail Image

Files

isbn9789526428345.pdf (6.63 MB)

URL

Journal Title

Journal ISSN

Volume Title

School of Engineering | Doctoral thesis (article-based) | Defence date: 2025-12-18
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

Date

2025

Department

Rakennustekniikan laitos
Department of Civil Engineering

Major/Subject

Mcode

Degree programme

Language

en

Pages

59 + app. 78

Series

Aalto University publication series Doctoral Theses, 231/2025

Abstract

Structural resilience and safety are critical concerns in modern infrastructure, particularly in the face of increasing seismic risks and aging buildings. While advanced artificial intelligence (AI) techniques offer promising solutions for structural design, damage assessment, and retrofitting, the "black-box" nature of many AI models limits their trustworthiness and adoption in safety-critical fields. This research integrates explainable AI (XAI) techniques to bridge the gap between complex machine learning models and engineering practice, developing innovative frameworks for enhanced interpretability in civil engineering applications. A primary contribution is Seismo-XAI, a web-based tool that delivers transparent, interpretable insights into building performance under seismic loads, empowering engineers to optimize designs and retrofits. The study also introduces a novel hybrid framework combining microstructure-informed finite element modelling with computer vision to advance damage classification in concrete, synergizing vision transformers' computational efficiency with physics-based finite element analysis. To address data limitations, a synthetic dataset of concrete microstructures was generated using stochastic Monte Carlo augmentation. For uncertainty quantification in seismic assessment, this research employs artificial neural networks with the quality-driven lower upper bound estimation method to establish prediction intervals for long-term ground motion effects. This distribution-free machine learning approach provides both local and global levels probabilistic damage assessment, enhancing reliability in post-earthquake evaluations and early warning systems. Validation through benchmark structures and real-world case studies demonstrates the frameworks’ effectiveness in promoting reliable design practices for seismic resilience and damage assessment. The findings highlight the potential of XAI to transform structural engineering by providing transparent, interpretable, and actionable insights, fostering trust in AI-driven solutions. This research lays the foundation for future advancements in and development of AI applications for structural health monitoring and disaster management for urban infrastructure.

Description

Supervising professor

Noureldin, Mohamed, Assist. Prof., Aalto University, Department of Civil Engineering, Finland

Thesis advisor

Noureldin, Mohamed, Assist. Prof., Aalto University, Department of Civil Engineering, Finland

Keywords

explainable artificial intelligence, explainability, interpretability, performance-based seismic engineering design, reinforced concrete structures, uncertainty quantification

Other note

Parts

  • [Publication 1]: Shabbir Khurram; Noureldin Mohamed; Sim Sung‐Han. 2025. Data‐driven model for seismic assessment, design, and retrofit of structures using explainable artificial intelligence. Computer‐Aided Civil and Infrastructure Engineering, 40(3), pp.281-300.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202501101078
    DOI: 10.1111/mice.13338 View at publisher
  • [Publication 2]: Shabbir Khurram; Yossef Mostafa; Noureldin Mohamed. 2025. A hybrid vision transformer and finite element framework for explainable concrete damage classification. Journal of Building Engineering, (Under review, submission date: October 22, 2025)
  • [Publication 3]: Shabbir Khurram; Umair Muhammed; Sim Sung‐Han; Ali Usman; Noureldin Mohamed. 2024. Estimation of Prediction Intervals for Performance Assessment of Building Using Machine Learning. Sensors, 24(13), p.4218.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202408095397
    DOI: 10.3390/s24134218 View at publisher

Citation

Permanent link to this item

https://urn.fi/URN:ISBN:978-952-64-2834-5

Collections

[diss] Insinööritieteiden korkeakoulu / ENG