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- 2023
-
Mark
Indoor radon interval prediction in the Swedish building stock using machine learning
(
- Contribution to journal › Article
-
Mark
Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings
(
- Contribution to journal › Article
-
Mark
Estimating the probability distributions of radioactive concrete in the building stock using Bayesian networks
2023) In Expert Systems with Applications(
- Contribution to journal › Article
-
Mark
Evaluating the Indoor Radon Concentrations in the Swedish Building Stock Using Statistical and Machine Learning
(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
- 2022
-
Mark
Modeling Artificial Neural Networks to Predict Asbestos-containing Materials in Residential Buildings
2022)(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
-
Mark
Predicting the presence of hazardous materials in buildings using machine learning
(
- Contribution to journal › Article
- 2021
-
Mark
Approach to manage parameter and choice uncertainty in life cycle optimisation of building design : Case study of optimal insulation thickness
(
- Contribution to journal › Article
-
Mark
Determining the Impact of High Residential Density on Indoor Environment, Energy Use, and Moisture Loads in Swedish Apartments-and Measures for Mitigation
(
- Contribution to journal › Article
-
Mark
Tracing Hazardous Materials in Registered Records: A Case Study of Demolished and Renovated Buildings in Gothenburg
2021) 2069.(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
-
Mark
The effect of weighting factors on income-related energy inequalities : The case of Sweden’s new building code
(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding