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Pei-Yu Wu

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Assistant Professor

Delft University of Technology

Netherlands

Research Interests

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Professors in Netherlands

About

Pei-Yu Wu is an Assistant Professor at Delft University of Technology in the Netherlands. Her research focuses on predictive modeling and the application of machine learning techniques in building hazard assessments, particularly regarding moisture damage and hazardous materials in the building stock. Recent articles include studies on estimating radioactive concrete distributions and indoor radon prediction, contributing significantly to the field of environmental safety in architecture.

Recent Grants

Grant: Close

Development of machine learning-embedded applications to support hazardous material assessment in renovation and demolition

Open Date: 2022-10-01

Close Date: 2023-12-01

Grant: Close

Data-driven approach to trace and evaluate the recycling potential of PVC flooring in the building stock

Open Date: 2022-10-01

Close Date: 2023-12-01

Grant: Close

Predicting moisture damage in existing and new buildings with the help of AI (machine learning)

Open Date: 2022-10-01

Close Date: 2024-06-01

Articles (13)

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2026

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2026

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2025

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2025

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2025

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2025

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2023

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2023

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2023

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2022

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2021

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2021

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2021

Collaborators (4)

Mikael Mangold

Docent

Malmö University

SWEDEN

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Catherine De Wolf

Assistant Professor of Circular Engineering for Architecture

ETH Zürich

SWITZERLAND

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Mennatallah El‐Assady

Assistant Professor

GERMANY

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Petter Wallenten

Senior Lecturer

Lund University

SWEDEN

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Pei-Yu Wu

Research Interests

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About

Recent Grants

Articles (13)

A unified intelligence-augmented framework for building audits via physical and virtual inspection

Quantifying the impact of inadequate building management and maintenance on damage, failures and functional defects

Predictive modeling and estimation of moisture damages in Swedish buildings: A machine learning approach

Empirical Quantification and Prediction of Building Component Lifespans with Graph Neural Networks

Fostering creativity using AI towards a circular economy in architectural engineering design

Probabilistic Distributions of Moisture Damages in Swedish Buildings

Estimating the probability distributions of radioactive concrete in the building stock using Bayesian networks

Indoor radon interval prediction in the Swedish building stock using machine learning

Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings

Predicting the presence of hazardous materials in buildings using machine learning

Tracing Hazardous Materials in Registered Records: A Case Study of Demolished and Renovated Buildings in Gothenburg

Machine Learning in Hazardous Building Material Management: Research Status and Applications

A data-driven approach to assess the risk of encountering hazardous materials in the building stock based on environmental inventories

Collaborators (4)

Mikael Mangold

Catherine De Wolf

Mennatallah El‐Assady

Petter Wallenten

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