Algorithm for automatic detection and analysis of cracks in timber beams from LiDAR data

M. Cabaleiro; R. Lindenbergh; W. F. Gard; P. Arias; J. W G van de Kuilen

doi:10.1016/j.conbuildmat.2016.11.032

Algorithm for automatic detection and analysis of cracks in timber beams from LiDAR data

M. Cabaleiro^*, R. Lindenbergh, W. F. Gard, P. Arias, J. W G van de Kuilen

^*Corresponding author for this work

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Abstract

Cracks may indicate structural problems in constructions made of timber beams, but their identification in notably roof constructions is difficult. Therefore this paper presents an algorithm for the automatic detection of cracks in timber beams sampled by LiDAR data. This algorithm enables the identification, analysis and monitoring of cracks and their geometrical characteristics. The algorithm is validated by tests on laboratory specimens and on a timber roof structure. The results prove that the proposed algorithm is able to automatically identify and analyze cracks with a width of above 3 mm from point clouds with an average resolution below 1 mm.

Original language	English
Pages (from-to)	41-53
Number of pages	13
Journal	Construction and Building Materials
Volume	130
DOIs	https://doi.org/10.1016/j.conbuildmat.2016.11.032
Publication status	Published - 15 Jan 2017

Keywords

Alpha-shapes
Crack
Laser scanning
Wood

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10.1016/j.conbuildmat.2016.11.032

Review manuscript CracksAccepted author manuscript, 6.56 MBLicence: CC BY-NC-ND

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title = "Algorithm for automatic detection and analysis of cracks in timber beams from LiDAR data",

abstract = "Cracks may indicate structural problems in constructions made of timber beams, but their identification in notably roof constructions is difficult. Therefore this paper presents an algorithm for the automatic detection of cracks in timber beams sampled by LiDAR data. This algorithm enables the identification, analysis and monitoring of cracks and their geometrical characteristics. The algorithm is validated by tests on laboratory specimens and on a timber roof structure. The results prove that the proposed algorithm is able to automatically identify and analyze cracks with a width of above 3 mm from point clouds with an average resolution below 1 mm.",

keywords = "Alpha-shapes, Crack, Laser scanning, Wood",

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AU - Cabaleiro, M.

AU - Lindenbergh, R.

AU - Gard, W. F.

AU - Arias, P.

AU - van de Kuilen, J. W G

PY - 2017/1/15

Y1 - 2017/1/15

N2 - Cracks may indicate structural problems in constructions made of timber beams, but their identification in notably roof constructions is difficult. Therefore this paper presents an algorithm for the automatic detection of cracks in timber beams sampled by LiDAR data. This algorithm enables the identification, analysis and monitoring of cracks and their geometrical characteristics. The algorithm is validated by tests on laboratory specimens and on a timber roof structure. The results prove that the proposed algorithm is able to automatically identify and analyze cracks with a width of above 3 mm from point clouds with an average resolution below 1 mm.

AB - Cracks may indicate structural problems in constructions made of timber beams, but their identification in notably roof constructions is difficult. Therefore this paper presents an algorithm for the automatic detection of cracks in timber beams sampled by LiDAR data. This algorithm enables the identification, analysis and monitoring of cracks and their geometrical characteristics. The algorithm is validated by tests on laboratory specimens and on a timber roof structure. The results prove that the proposed algorithm is able to automatically identify and analyze cracks with a width of above 3 mm from point clouds with an average resolution below 1 mm.

KW - Alpha-shapes

KW - Crack

KW - Laser scanning

KW - Wood

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DO - 10.1016/j.conbuildmat.2016.11.032

M3 - Article

SN - 0950-0618

VL - 130

SP - 41

EP - 53

JO - Construction and Building Materials

JF - Construction and Building Materials

ER -

Algorithm for automatic detection and analysis of cracks in timber beams from LiDAR data

Abstract

Keywords

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