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

Pei-Yu Wu; Kristina Mjörnell; Claes  Sandels; Mikael Mangold

doi:10.21926/rpm.2102017

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

Pei-Yu Wu, Kristina Mjörnell, Claes Sandels, Mikael Mangold

Avdelningen för Byggnadsfysik

Research Institutes of Sweden (RISE)

Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift › Peer review

Originalspråk	engelska
Tidskrift	Recent Progress in Materials
Volym	3
Nummer	2
DOI	https://doi.org/10.21926/rpm.2102017
Status	Published - 2021 maj 11

Ämnesklassifikation (UKÄ)

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10.21926/rpm.2102017

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

1 Licentiatavhandling

Predicting hazardous materials in the Swedish building stock using data mining
Wu, P.-Y., 2022 jan. 14, Lund: Lund University. 110 s.
Forskningsoutput: Avhandling › Licentiatavhandling

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@article{a9df176dd26f49dba12a5555e0369d3f,

title = "Machine Learning in Hazardous Building Material Management: Research Status and Applications",

author = "Pei-Yu Wu and Kristina Mj{\"o}rnell and Claes Sandels and Mikael Mangold",

year = "2021",

month = may,

day = "11",

doi = "10.21926/rpm.2102017",

language = "English",

volume = "3",

journal = "Recent Progress in Materials",

issn = "2689-5846",

publisher = "LIDSEN Publishing",

number = "2",

}

TY - JOUR

T1 - Machine Learning in Hazardous Building Material Management

T2 - Research Status and Applications

AU - Wu, Pei-Yu

AU - Mjörnell, Kristina

AU - Sandels, Claes

AU - Mangold, Mikael

PY - 2021/5/11

Y1 - 2021/5/11

U2 - 10.21926/rpm.2102017

DO - 10.21926/rpm.2102017

M3 - Article

SN - 2689-5846

VL - 3

JO - Recent Progress in Materials

JF - Recent Progress in Materials

IS - 2

ER -

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

Ämnesklassifikation (UKÄ)

Tillgång till dokument

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Forskningsoutput

Predicting hazardous materials in the Swedish building stock using data mining

Citera det här