Automatic segmentation of 3D point clouds of rubble masonry walls, and its application to building surveying, repair and maintenance

Valero, Enrique; Bosché, Frédéric; Förster, A.

doi:10.1016/j.autcon.2018.08.018

Cited by 81 publications

(35 citation statements)

References 12 publications

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“…In the architectural heritage field, the process of structuring and classifying 3D survey data have seen various applications such as supporting the scan-to-BIM process [22][23][24][25], monitoring and restoration purposes [3,6,26], maintenance architecture planning [27,28], and damage detection and quantification [29][30][31].…”

Section: State Of the Artmentioning

confidence: 99%

A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

et al. 2020

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The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution.

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Section: State Of the Artmentioning

confidence: 99%

A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

et al. 2020

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“…This facilitates subsequent repair and maintenance tasks. Research of this nature has been further developed by Bosche, Valero and Forster [22,23] who wrote bespoke algorithms for the automatic segmentation of individual masonry units. These works have been produced in support of survey and evaluation of historic fabric on a finer level of detail, enabling the subsequent improvement in inspection, diagnosis, and repair decisions-making [24].…”

Section: Processing Reality Capture Data For Supporting Surveyorsmentioning

confidence: 99%

“…Primary operations commonly encountered are: the replacement or consolidation of ashlar stone and objective quantification of these targeted interventions, and the accurate estimation of specified materials. A novel and more precise analysis of fabric at different levels of detail (as presented in [23,24]) is performed here. The segmentation of masonry into ashlar units presents several advantages with respect to previous works (e.g.…”

Section: Contributionmentioning

confidence: 99%

Automated defect detection and classification in ashlar masonry walls using machine learning

Valero

Förster

Bosché

et al. 2019

Automation in Construction

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118

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Methods employed for surveying buildings for condition have traditionally been reliant upon visual assessment and manual recording. Survey of traditional masonry also ostensibly conforms to this approach but, due to the sheer volume of masonry units composing walls, it is often prohibitively time consuming, exceptionally complex and ultimately costly. Notable features of such survey work for ashlar stone types require each stone to be labelled and overlaid with information relative to condition. Further hindering these already costly operations, it has been shown that the accuracy of reporting, including labelling the manifestation of defects and defect diagnosis, is subjective, depending upon the expertise and experience of those evaluating the fabric. Moving beyond these preliminary survey and reporting stages, this situation gives rise to variable repair and maintenance strategies that can have significant cost implications and can debase fundamental conservation activities. The development of digital technologies, such as terrestrial laser scanning, and advancements in novel computer vision statistical techniques can help produce accurate representation of buildings that can be subsequently rapidly processed, achieving many tangible survey functions with greater inherent objectivity. In this paper, an innovative strategy for automatic detection and classification of defects in digitised ashlar masonry walling is presented. The classification method is based on the use of supervised machine learning algorithms, assisted by surveyors' strategies and expertise to identify defective individual masonry units, through to broader global patterns for groups of stones. The proposed approach has been tested on the main façade of the Chapel Royal in Stirling Castle (Scotland), demonstrating its potential for ashlar masonry forms of wall construction. It is important to recognise that the findings are not limited to this culturally significant building and will be of high value to almost innumerable ashlar-built structures worldwide. The research ultimately attempts to reduce the degree of subjectivity in classifying defects, on a scale and rapidity hitherto beyond traditional project cost constraints. Importantly, it is recognised that through automation more effective utilisation of resources that would have been traditionally spent on survey can be redeployed to support fabric intervention or routine maintenance operations.

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“…Raster images can be obtained from rectified photography [18] or orthophotos generated using photogrammetry [19], while vectors are manually drawn by mapping the former in computer-aided design (CAD) programs. CV algorithms segment masonry units using point clouds and images [20][21][22]. However, manual operations may still be necessary given the complexity of the masonry surfaces that have experienced damages and alternations.…”

Section: Existing Approaches Based On Cad and Gismentioning

confidence: 99%

As-Built BIM for a Fifteenth-Century Chinese Brick Structure at Various LoDs

Sun

Xie

Zhang

et al. 2019

IJGI

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Building information modeling (BIM) has received significant research attention in the field of built heritage. As-built BIM refers to a BIM representation of the "as-is" conditions of built heritage at the time of a survey. Determining the level of development (LoD) is crucial for as-built BIM owing to its relevance to model effects and modeling efforts. This study addresses this issue from the viewpoint of a brick structure based on a case study of a fifteenth-century ruin in Nanjing, China. Three LoDs are proposed based on the combined use of a commercial platform and auxiliary tools: A host model linked with raster images composed using orthoimage and relief maps (LoD 1), an as-built volume with semantic skins (LoD 2), and a brick-by-brick model with custom industry foundation class parameters at local areas (LoD 3). The results reveal that LoD 1 caters to an efficient web-based workflow for brick-damage annotations; as-built dimensions can be extracted from LoD 2; and LoD 3 enables attributes, such as damage types, to be attached at the brick level. In future studies, the detection of brick shapes is expected to automate the process of as-built surface mapping.

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Automatic segmentation of 3D point clouds of rubble masonry walls, and its application to building surveying, repair and maintenance

Cited by 81 publications

References 12 publications

A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

Automated defect detection and classification in ashlar masonry walls using machine learning

As-Built BIM for a Fifteenth-Century Chinese Brick Structure at Various LoDs

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