Recognition of the condition of construction materials using small datasets and handcrafted features
| dc.contributor.author | Mengiste, Eyob | |
| dc.contributor.author | Garcia de Soto, Borja | |
| dc.contributor.author | Hartmann, Timo | |
| dc.date.accessioned | 2023-01-30T11:44:38Z | |
| dc.date.available | 2023-01-30T11:44:38Z | |
| dc.date.issued | 2022-11 | |
| dc.description.abstract | We propose using handcrafted features extracted from small datasets to classify the conditions of the construction materials. We hypothesize that features such as the color, roughness, and reflectance of a material surface can be used to identify details of the material. To test the hypothesis, we have developed a pre-trained model to classify material conditions based on reflectance, roughness and color features extracted from image data collected in a controlled (lab) environment. The knowledge learned in the pre-trained model is finally transferred to classify material conditions from a construction site (i.e., an uncontrolled environment). To demonstrate the proposed method, 80 data points were produced from the images collected under a controlled environment and used to develop a pre-trained model. The pre-trained model was re-trained to adapt to the real construction environment using 33 new data points generated through a separate process using images collected from a construction site. The pre-trained model achieved 93%; after retraining the model with the data from the actual site, the accuracy had a small decrease as expected, but still was promising with an 83% accuracy. | en |
| dc.identifier.eissn | 1874-4753 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/18114 | |
| dc.identifier.uri | https://doi.org/10.14279/depositonce-16907 | |
| dc.language.iso | en | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject.ddc | 690 Hausbau, Bauhandwerk | de |
| dc.subject.other | image processing | en |
| dc.subject.other | transfer learning | en |
| dc.subject.other | roughness | en |
| dc.subject.other | reflectance | en |
| dc.subject.other | color | en |
| dc.subject.other | CIELab | en |
| dc.subject.other | small datasets | en |
| dc.title | Recognition of the condition of construction materials using small datasets and handcrafted features | en |
| dc.type | Article | |
| dc.type.version | publishedVersion | |
| dcterms.bibliographicCitation.doi | 10.36680/j.itcon.2022.046 | |
| dcterms.bibliographicCitation.journaltitle | Journal of information technology in construction | |
| dcterms.bibliographicCitation.originalpublishername | Royal Institute of Technology | |
| dcterms.bibliographicCitation.originalpublisherplace | Auckland | |
| dcterms.bibliographicCitation.pageend | 971 | |
| dcterms.bibliographicCitation.pagestart | 951 | |
| dcterms.bibliographicCitation.volume | 27 | |
| dcterms.rightsHolder.reference | Creative-Commons-Lizenz | |
| tub.accessrights.dnb | free | |
| tub.affiliation | Fak. 6 Planen Bauen Umwelt::Inst. Bauingenieurwesen::FG Systemtechnik baulicher Anlagen | |
| tub.publisher.universityorinstitution | Technische Universität Berlin |