میز خدمت الکترونیک
دوره ویراستاری

  اخبار و اعلانات

 آمار

تعداد نشریات127
تعداد شماره‌ها7,196
تعداد مقالات77,227
تعداد مشاهده مقاله157,218,075
تعداد دریافت فایل اصل مقاله118,403,692
Tsang, Long, Zanjirani Farahani, Hosein, Farahani, Atiye, Ghorbani, Ali. (1404). Rock sample strength evaluation using a series of machine learning methods. , (), -. doi: 10.22059/ceij.2026.394143.2304
Long Tsang; Hosein Zanjirani Farahani; Atiye Farahani; Ali Ghorbani. "Rock sample strength evaluation using a series of machine learning methods". , , , 1404, -. doi: 10.22059/ceij.2026.394143.2304
Tsang, Long, Zanjirani Farahani, Hosein, Farahani, Atiye, Ghorbani, Ali. (1404). 'Rock sample strength evaluation using a series of machine learning methods', , (), pp. -. doi: 10.22059/ceij.2026.394143.2304
Tsang, Long, Zanjirani Farahani, Hosein, Farahani, Atiye, Ghorbani, Ali. Rock sample strength evaluation using a series of machine learning methods. , 1404; (): -. doi: 10.22059/ceij.2026.394143.2304

Rock sample strength evaluation using a series of machine learning methods

Civil Engineering Infrastructures Journal
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 08 بهمن 1404    اصل مقاله (1.52 M)
شناسه دیجیتال (DOI): 10.22059/ceij.2026.394143.2304
نویسندگان
Long Tsang1؛ Hosein Zanjirani Farahani2؛ Atiye Farahani* 3؛ Ali Ghorbani4
1Geofirst Pty Ltd., 2/7 Luso Drive, Unanderra, NSW 2526, Australia
2Assistant Professor, Department of Civil Engineering, Tafresh University, Tafresh, Iran
3Assistant Professor, Department of Civil Engineering, Technical and Vocational University (TVU), Tehran, Iran
4Assistant Professor, Department of Engineering, Payame Noor University, Tehran, Iran
چکیده
Rock engineering tasks including tunneling, dam building, and ensuring rock slope stability rely heavily on the uniaxial compressive strength (UCS) as a key geomechanical metric. The primary goal of this research was to compare the accuracy of the random forest (RF), k-nearest neighbors (kNN), the decision tree (DT), and the Adaboost in predicting the various rock UCS samples. The approaches were applied to 170 data sets, including point load index (Is(50)), porosity (n), Schmidt hammer (SH), and P-wave velocity (Vp). Initially, the 4 outlier data techniqes were implemented to improve the effectiveness of the used approaches. Then, using the selected data, 4 different machine learning models were developed to predict UCS. Based on different criteria, the 4 models were compared with each other, among which the Adaboost model provided the best response. This model provided R2 values of 0.9631, RMSE of 9.781 and an MAE of 3.684 for the training part and R2 values of 0.9326, RMSE of 13.234 and an MAE of 9.656 for the testing part. Finally, two parameters porosity (n) and Schmidt hammer (SH) were introduced as the most influential parameters in these models.
کلیدواژه‌ها
Uniaxial compressive strength؛ Rock؛ Machine learning؛ Random forest؛ Adaboost
آمار
تعداد مشاهده مقاله: 113
تعداد دریافت فایل اصل مقاله: 110





سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب