Using support vector machines to classify building elements for checking the semantic integrity of building information models

Koo, Bon Seok; La, Sunmin; Cho, Nam-Wook; Yu, Young-Su

doi:10.1016/j.autcon.2018.11.015

Cited by 67 publications

(18 citation statements)

References 23 publications

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“…Using the single features and pairwise features of space objects, the machine learning algorithm is trained to classify the type of room [2]. Koo et al utilized support vector machines, which is one of the machine learning algorithms, to classify the building objects and check the semantic integrity of them [8]. These studies tried to switch the rule-based inference method into machine learning-based method, and showed the potential of the approaches.…”

Section: Semantic Information Of Building Objects For Domain-specificmentioning

confidence: 99%

“…Space is one of the critical elements in the computerbased information system for the concept design, construction process, and facility management process [5,8]. As a functional element where human activities are performed, space is used as a unit of design or building analysis.…”

Section: Spatial Category Classificationmentioning

confidence: 99%

“…In order to intelligently automate these reasoning process by a computer, there have been some researchers to implement the automated information inference system. The research area, called semantic enrichment, addressed the interoperability of semantic BIM data by employing rule-based or machine learning methodologies [2,[5][6][7][8]. This research is also a part of them, which tried to implement the semantic enrichment system focusing on a space object instance.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Information Enrichment using NLP-based Classification of Space Objects for School Bldgs. in Korea

Song¹,

Kim²,

Lee³

2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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This paper presents an approach to classifying spatial categories of space objects using their textual properties in IFC data. As a standardized data format of building information, IFC enhances the data interoperability between the heterogeneous domain software. However, there are some proble ms that required information is omitted due to the technical translation error or mistake by users. The other problem is that some semantic information cannot be defined in the IFC data sche me. Manually checking and modifying this information requires an amount of time and labor. The intelligent information enrichme nt system can facilitate the application of BIM. In this regards, this research tried to address the interoperability problems with a machine learning-based method by automatically enhancing the semantic information of the BIM model. In this paper, we focused on the semantic enrichme nt of space objects by using textual data in the IFC. We implemented the NLP-based classification training model, which e mploys the word e mbedding techniques. As an early phase of research, this paper conducted training experiments with variable input properties, name and area of space, from space object in Korea school buildings. The accuracy of the classification model with a single feature (name) is measured at 85.9%, which is higher than multi feature (name and area). The suggested model can be expanded to more variable properties and target objects as a part of the semantic enrichment system.

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Section: Semantic Information Of Building Objects For Domain-specificmentioning

confidence: 99%

Section: Spatial Category Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial Information Enrichment using NLP-based Classification of Space Objects for School Bldgs. in Korea

Song¹,

Kim²,

Lee³

2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…Apart from estimating compressive strength, the SVM is used for several applications in engineering, such as traffic sign detection [33], modelling soil pollution [34], predicting daily flow of river [35] predicting elastic modulus of concrete [36], modelling landslide susceptibility [37], air balancing for ventilation systems [38] and predicting shear force for base isolation device [39,40]. Its other successful applications include for example, classifying building information modelling elements [41], system reliability analysis of slopes [42], estimation of concrete expansion caused by alkali-aggregate reaction [43], damage detection in a three-story frame structure [44], prediction of lateral load capacity of piles [45], crack inspection for aircraft skin [46] and assessing liquefaction potential [47].…”

Section: Introductionmentioning

confidence: 99%

Support vector machine for determining the compressive strength of brick-mortar masonry using NDT data fusion (case study: Kharagpur, India)

2019

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The accurate prediction of compressive strength of brick-mortar masonry walls is crucial for the damage assessment of load-bearing masonry constructions. Direct tests conducted to estimate compressive strength involve core drilling and are expensive. To estimate compressive strength, several indirect test parameters can be used as empirical predictors. Nondestructive tests can be rapidly executed, can significantly reduce repair costs, and can increase the knowledge level of buildings by indirectly estimating compressive strength. This study aimed to determine the compressive strength of masonry construction by using support vector machines (SVMs). Input variables of the model are test data obtained from the nondestructive and destructive testing of 44 masonry wallettes cast in a laboratory for evaluating the compressive strength of brick (f b), rebound hammer number, and ultrasonic pulse velocity, while the compressive strength of the wall (f c) is output. The final results obtained using an SVM model are validated for a masonry building in Kharagpur, India through experimental testing, and these results are compared with other established empirical relationships. The results indicate that the SVM can be efficiently used to predict the compressive strength of brick-mortar masonry.

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“…1). The technique has been used to solve different problems in engineering [24]- [26], used in normal-weight concrete [27], [28] as well as in LWAC [29]. Hariri-Ardebili and Pourkamali-Anaraki [24].…”

Section: Introductionmentioning

confidence: 99%

Compressive Strength Classification of Lightweight Aggregate Concrete Using a Support Vector Machine Model

Tenza-Abril

Satorre-Cuerda

Compañ

et al. 2019

Computational Methods and Experimental Measurements XIX

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Lightweight aggregates (LWA) are used to produce low-density concretes required for building applications. Lightweight aggregate concrete (LWAC) is a multipurpose material for construction, which offers technical, economical and environment benefits, and it is produced by replacing the normal-weight aggregates with LWA, depending upon the requirements of density and strength. LWAC is a complex composite material, and a model of its compressive strength must be highly nonlinear because it is very sensitive to its ingredients, so modelling its behaviour is a difficult task. Many studies have tried to develop accurate and effective predictive models for LWAC compressive strength. In this study, a support vector machine (SVM) learning algorithm is used to propose a model to classify the compressive strength of a wide range of LWAC. A dataset of 241 different LWACs were used for classifying the compressive strength into six different classes (from low-strength to high-strength) using different variables-quantity of cement, water and LWA in the dosage and density of the LWAC produced. The results show that increasing the variables means the model becomes more accurate up to approximately an 80% rate of success. The SVM model proved to be a significant tool to classify the compressive strength of LWAC contributing to engineers avoiding costly experimental trial tests.

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Using support vector machines to classify building elements for checking the semantic integrity of building information models

Cited by 67 publications

References 23 publications

Spatial Information Enrichment using NLP-based Classification of Space Objects for School Bldgs. in Korea

Spatial Information Enrichment using NLP-based Classification of Space Objects for School Bldgs. in Korea

Support vector machine for determining the compressive strength of brick-mortar masonry using NDT data fusion (case study: Kharagpur, India)

Compressive Strength Classification of Lightweight Aggregate Concrete Using a Support Vector Machine Model

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