Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/68119
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dc.contributor.authorXuan Nam Buien_US
dc.contributor.authorPirat Jaroonpattanapongen_US
dc.contributor.authorHoang Nguyenen_US
dc.contributor.authorQuang Hieu Tranen_US
dc.contributor.authorNguyen Quoc Longen_US
dc.date.accessioned2020-04-02T15:21:01Z-
dc.date.available2020-04-02T15:21:01Z-
dc.date.issued2019-12-01en_US
dc.identifier.issn20452322en_US
dc.identifier.other2-s2.0-85072694430en_US
dc.identifier.other10.1038/s41598-019-50262-5en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85072694430&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/68119-
dc.description.abstract© 2019, The Author(s). In this scientific report, a new technique of artificial intelligence which is based on k-nearest neighbors (KNN) and particle swarm optimization (PSO), named as PSO-KNN, was developed and proposed for estimating blast-induced ground vibration (PPV). In the proposed PSO-KNN, the hyper-parameters of the KNN were searched and optimized by the PSO. Accordingly, three forms of kernel function of the KNN were used, Quartic (Q), Tri weight (T), and Cosine (C), which result in three models and abbreviated as PSO-KNN-Q, PSO-KNN-T, and PSO-KNN-C models. The valid of the proposed models was surveyed through comparing with those of benchmarks, random forest (RF), support vector regression (SVR), and an empirical technique. A total of 152 blasting events were recorded and analyzed for this aim. Herein, maximum explosive per blast delay (W) and the distance of PPV measurement (R), were used as the two input parameters for predicting PPV. RMSE, R2, and MAE were utilized as performance indicators for evaluating the models’ accuracy. The outcomes instruct that the PSO algorithm significantly improved the efficiency of the PSO-KNN-Q, PSO-KNN-T, and PSO-KNN-C models. Compared to the three benchmarks models (i.e., RF, SVR, and empirical), the PSO-KNN-T model (RMSE = 0.797, R2 = 0.977, and MAE = 0.385) performed better; therefore, it can be introduced as a powerful tool, which can be used in practical blasting for reducing unwanted elements induced by PPV in surface mines.en_US
dc.subjectMultidisciplinaryen_US
dc.titleA Novel Hybrid Model for Predicting Blast-Induced Ground Vibration Based on k-Nearest Neighbors and Particle Swarm Optimizationen_US
dc.typeJournalen_US
article.title.sourcetitleScientific Reportsen_US
article.volume9en_US
article.stream.affiliationsDuy Tan Universityen_US
article.stream.affiliationsHanoi Universityen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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