Planning of Deep Foundation Construction Technical Specifications Using Improved Case-Based Reasoning with Weighted k -Nearest Neighbors
© 2017 American Society of Civil Engineers. Planning of construction technical specifications (CTS) for deep foundations is critical for ensuring works performed safely. k-nearest neighbors (kNN) is regarded as a practical algorithm for case retrieval in a case-based reasoning (CBR) cycle to search...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
American Society of Civil Engineering
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/58401 |
| _version_ | 1848760250962804736 |
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| author | Zhang, Y. Ding, L. Love, Peter |
| author_facet | Zhang, Y. Ding, L. Love, Peter |
| author_sort | Zhang, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017 American Society of Civil Engineers. Planning of construction technical specifications (CTS) for deep foundations is critical for ensuring works performed safely. k-nearest neighbors (kNN) is regarded as a practical algorithm for case retrieval in a case-based reasoning (CBR) cycle to search for past similar plans for new plan making. The parameter k and neighbors' weights affect the performance of the CBR cycle deeply but kNN neglects the weights' effect on case retrieval. The massive and multisource data of CTS of deep foundations presents a challenge for retaining case data in a database and for decision making due to an inefficient data process of the traditional tool. This paper presents a new framework to integrate weighted k-nearest neighbors (kkNN) to improve the performance of a CBR system for technical planning of deep foundations. It contains two parts: (1) a process to deal with a large amount of data derived from CTS; and (2) kkNN to obtain similar cases considering k and the weights of neighbors'. The feasibility of the proposed approach is validated through a case study and the evaluation result shows that the approach enhances the performance of the CBR cycle in creating construction technical specifications in deep foundation projects. |
| first_indexed | 2025-11-14T10:12:48Z |
| format | Journal Article |
| id | curtin-20.500.11937-58401 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:12:48Z |
| publishDate | 2017 |
| publisher | American Society of Civil Engineering |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-584012017-11-24T05:45:44Z Planning of Deep Foundation Construction Technical Specifications Using Improved Case-Based Reasoning with Weighted k -Nearest Neighbors Zhang, Y. Ding, L. Love, Peter © 2017 American Society of Civil Engineers. Planning of construction technical specifications (CTS) for deep foundations is critical for ensuring works performed safely. k-nearest neighbors (kNN) is regarded as a practical algorithm for case retrieval in a case-based reasoning (CBR) cycle to search for past similar plans for new plan making. The parameter k and neighbors' weights affect the performance of the CBR cycle deeply but kNN neglects the weights' effect on case retrieval. The massive and multisource data of CTS of deep foundations presents a challenge for retaining case data in a database and for decision making due to an inefficient data process of the traditional tool. This paper presents a new framework to integrate weighted k-nearest neighbors (kkNN) to improve the performance of a CBR system for technical planning of deep foundations. It contains two parts: (1) a process to deal with a large amount of data derived from CTS; and (2) kkNN to obtain similar cases considering k and the weights of neighbors'. The feasibility of the proposed approach is validated through a case study and the evaluation result shows that the approach enhances the performance of the CBR cycle in creating construction technical specifications in deep foundation projects. 2017 Journal Article http://hdl.handle.net/20.500.11937/58401 10.1061/(ASCE)CP.1943-5487.0000682 American Society of Civil Engineering restricted |
| spellingShingle | Zhang, Y. Ding, L. Love, Peter Planning of Deep Foundation Construction Technical Specifications Using Improved Case-Based Reasoning with Weighted k -Nearest Neighbors |
| title | Planning of Deep Foundation Construction Technical Specifications Using Improved Case-Based Reasoning with Weighted k -Nearest Neighbors |
| title_full | Planning of Deep Foundation Construction Technical Specifications Using Improved Case-Based Reasoning with Weighted k -Nearest Neighbors |
| title_fullStr | Planning of Deep Foundation Construction Technical Specifications Using Improved Case-Based Reasoning with Weighted k -Nearest Neighbors |
| title_full_unstemmed | Planning of Deep Foundation Construction Technical Specifications Using Improved Case-Based Reasoning with Weighted k -Nearest Neighbors |
| title_short | Planning of Deep Foundation Construction Technical Specifications Using Improved Case-Based Reasoning with Weighted k -Nearest Neighbors |
| title_sort | planning of deep foundation construction technical specifications using improved case-based reasoning with weighted k -nearest neighbors |
| url | http://hdl.handle.net/20.500.11937/58401 |