Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
Blasting is routinely carried out in urban quarry sites. Residents or houses around quarry sites are affected by the ground vibrations induced by blasting. Peak Particle Velocity (PPV) is used as a metric to measure ground vibration intensity. Therefore, many prediction models of PPV using experimen...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/23/12674 |
| _version_ | 1797400459839799296 |
|---|---|
| author | Hajime Ikeda Masato Takeuchi Elsa Pansilvania Brian Bino Sinaice Hisatoshi Toriya Tsuyoshi Adachi Youhei Kawamura |
| author_facet | Hajime Ikeda Masato Takeuchi Elsa Pansilvania Brian Bino Sinaice Hisatoshi Toriya Tsuyoshi Adachi Youhei Kawamura |
| author_sort | Hajime Ikeda |
| collection | DOAJ |
| description | Blasting is routinely carried out in urban quarry sites. Residents or houses around quarry sites are affected by the ground vibrations induced by blasting. Peak Particle Velocity (PPV) is used as a metric to measure ground vibration intensity. Therefore, many prediction models of PPV using experimental methods, statistical methods, and Artificial Neural Networks (ANNs) have been proposed to mitigate this effect. However, prediction models using experimental and statistical methods have a tendency of poor prediction accuracy. In addition, while prediction models using ANNs can produce a highly accurate prediction results, a large amount of measured data is necessarily collected. In an urban quarry site where the number of blastings is limited, it is difficult to collect a lot of measured data. In this study, a new PPV prediction method using Weighted Non-negative Matrix Factorization (WNMF) is proposed. WNMF is a method that approximates a non-negative matrix (including missing data) to the product of two low-dimensional matrices and predicts the missing data. In addition, WNMF is one of the unsupervised learning methods, so it can predict PPV regardless of the amount of data. In this study, PPV was predicted using measured data from 100 sites at the Mikurahana quarry site in Japan. As a result, the proposed method showed higher accuracy when using measured data at 60 sites rather than 100 sites, and the root mean square error for PPV prediction decreased from 0.1759 (100 points) to 0.1378 (60 points). |
| first_indexed | 2024-03-09T01:55:50Z |
| format | Article |
| id | doaj.art-82d230b70ff44263a1b20e0d2e06309c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T01:55:50Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-82d230b70ff44263a1b20e0d2e06309c2023-12-08T15:11:21ZengMDPI AGApplied Sciences2076-34172023-11-0113231267410.3390/app132312674Urban Quarry Ground Vibration Forecasting: A Matrix Factorization ApproachHajime Ikeda0Masato Takeuchi1Elsa Pansilvania2Brian Bino Sinaice3Hisatoshi Toriya4Tsuyoshi Adachi5Youhei Kawamura6Graduate School of International Resources, Akita University, 1-1, Tegatagakuen Machi, Akita City 0108502, JapanGraduate School of International Resources, Akita University, 1-1, Tegatagakuen Machi, Akita City 0108502, JapanDivision of Engineering, Instituto Superior Politécnico de Tete, National Road n° 7: Km 1, Matundo Neighbourhood, Tete City 362, MozambiqueGraduate School of International Resources, Akita University, 1-1, Tegatagakuen Machi, Akita City 0108502, JapanGraduate School of International Resources, Akita University, 1-1, Tegatagakuen Machi, Akita City 0108502, JapanGraduate School of International Resources, Akita University, 1-1, Tegatagakuen Machi, Akita City 0108502, JapanFaculty of Engineering, Division of Sustainable Resources, Hokkaido University, 13jyounishi8, Kita-ku, Sapporo City 0608628, JapanBlasting is routinely carried out in urban quarry sites. Residents or houses around quarry sites are affected by the ground vibrations induced by blasting. Peak Particle Velocity (PPV) is used as a metric to measure ground vibration intensity. Therefore, many prediction models of PPV using experimental methods, statistical methods, and Artificial Neural Networks (ANNs) have been proposed to mitigate this effect. However, prediction models using experimental and statistical methods have a tendency of poor prediction accuracy. In addition, while prediction models using ANNs can produce a highly accurate prediction results, a large amount of measured data is necessarily collected. In an urban quarry site where the number of blastings is limited, it is difficult to collect a lot of measured data. In this study, a new PPV prediction method using Weighted Non-negative Matrix Factorization (WNMF) is proposed. WNMF is a method that approximates a non-negative matrix (including missing data) to the product of two low-dimensional matrices and predicts the missing data. In addition, WNMF is one of the unsupervised learning methods, so it can predict PPV regardless of the amount of data. In this study, PPV was predicted using measured data from 100 sites at the Mikurahana quarry site in Japan. As a result, the proposed method showed higher accuracy when using measured data at 60 sites rather than 100 sites, and the root mean square error for PPV prediction decreased from 0.1759 (100 points) to 0.1378 (60 points).https://www.mdpi.com/2076-3417/13/23/12674blastingPeak Particle Velocity (PPV)prediction modelsurban quarry vibrationsWeighted Non-negative Matrix Factorization (WNMF) |
| spellingShingle | Hajime Ikeda Masato Takeuchi Elsa Pansilvania Brian Bino Sinaice Hisatoshi Toriya Tsuyoshi Adachi Youhei Kawamura Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach Applied Sciences blasting Peak Particle Velocity (PPV) prediction models urban quarry vibrations Weighted Non-negative Matrix Factorization (WNMF) |
| title | Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach |
| title_full | Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach |
| title_fullStr | Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach |
| title_full_unstemmed | Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach |
| title_short | Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach |
| title_sort | urban quarry ground vibration forecasting a matrix factorization approach |
| topic | blasting Peak Particle Velocity (PPV) prediction models urban quarry vibrations Weighted Non-negative Matrix Factorization (WNMF) |
| url | https://www.mdpi.com/2076-3417/13/23/12674 |
| work_keys_str_mv | AT hajimeikeda urbanquarrygroundvibrationforecastingamatrixfactorizationapproach AT masatotakeuchi urbanquarrygroundvibrationforecastingamatrixfactorizationapproach AT elsapansilvania urbanquarrygroundvibrationforecastingamatrixfactorizationapproach AT brianbinosinaice urbanquarrygroundvibrationforecastingamatrixfactorizationapproach AT hisatoshitoriya urbanquarrygroundvibrationforecastingamatrixfactorizationapproach AT tsuyoshiadachi urbanquarrygroundvibrationforecastingamatrixfactorizationapproach AT youheikawamura urbanquarrygroundvibrationforecastingamatrixfactorizationapproach |