Concrete Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds

Hadavandsiri, Zahra

Concrete Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds

dc.contributor.advisor	Lichti, Derek D.
dc.contributor.author	Hadavandsiri, Zahra
dc.contributor.committeemember	Shahbazi, Mozhdeh M.
dc.contributor.committeemember	Dawson, Peter C.
dc.date	2020-11
dc.date.accessioned	2020-05-06T17:44:21Z
dc.date.available	2020-05-06T17:44:21Z
dc.date.issued	2020-05-05
dc.description.abstract	Concrete structures endure damage and deterioration when subjected to human activities and natural hazards. Early detection of damage on concrete structures is vital to counter the side effects deriving from damage and to allow timely maintenance procedures. This thesis presents a novel approach for high-precision detection of damage on concrete surfaces using terrestrial laser scanner point clouds (PCs). At first, an unsupervised approach is developed that utilizes a robust version of principal component analysis (PCA) classification in order to distinguish between structural damage and outliers present in the data. Numerical simulations are conducted to develop a systematic point-wise defect classifier that automatically diagnoses the location of surface damage on the investigated region. The developed method examined on two real datasets, demonstrate the validity of the proposed systematic framework for reliable detection of damage of any type which makes roughness as small as 1 cm or larger on the surface of concrete structures captured with any laser-scanning PC with a minimum spatial resolution of 5 mm point spacing. At second, a supervised approach is developed that employs the outcome of the primary unsupervised classifier in order to accurately annotate the training data without the need for manual labeling. One flume of an aqueduct dataset was used for training the system. This machine learning-based model relies on a support vector machine (SVM) algorithm to train a point-wise defect classifier for locating the concrete damage. This yields an average classification precision and F1-score of 97.33% showing the potential of using machine learning for concrete damage detection. The performance of the prediction model was evaluated on three real datasets. The prediction model can successfully mirror the high performance of the unsupervised method used in the training process. In addition, by exploiting a more extensive variety of geometric features and skipping the intensive computation of the robust PCA, it outperforms the unsupervised classifier in terms of model performance and computational efficiency, respectively. Consequently, the properly trained machine learning system provides reliable diagnosis of the health conditions of large concrete structures that are not computationally feasible to be inspected by the primary unsupervised classifier.	en_US
dc.identifier.citation	Hadavandsiri, Z. (2020). Concrete Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/37803
dc.identifier.uri	http://hdl.handle.net/1880/111993
dc.language.iso	eng	en_US
dc.publisher.faculty	Schulich School of Engineering	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject.classification	Remote Sensing	en_US
dc.subject.classification	Statistics	en_US
dc.subject.classification	Computer Science	en_US
dc.title	Concrete Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds	en_US
dc.type	doctoral thesis	en_US
thesis.degree.discipline	Engineering – Geomatics	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Doctor of Philosophy (PhD)	en_US
ucalgary.item.requestcopy	false	en_US