20.500.12556/RUL-183227 Performance evaluation of machine learning methods for ground settlement prediction Ocena činkovitosti metod strojnega učenja za napoved posedanja tal Prediction of tunneling-induced ground settlements is an important task during tunnel excavation in urban areas. Ground settlements should be limited within a tolerable threshold to avoid damages to existing buildings and infrastructures during and after the construction. Machine learning (ML) methods have been gaining an increasing popularity in many fields, including tunnel excavations, as a powerful learning and predicting technique. The paper analyzes the possibilities of different machine learning methods to predict the ground surface settlement induced by tunneling. Three different ML approaches, including support vector regression (SVR), multilayer perceptron (MLP), and long short-term memory (LSTM) networks, are utilized. Two techniques are used for the hyperparameter optimization: particle swarm optimization (PSO) and grid search (GS) methods. To assess the performance of the ML methods, three performance metrics are used: the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The paper demonstrates the applicability of the three ML methods in tunneling-induced ground settlement prediction for real-world settlement datasets. The obtained experimental results indicate that the proposed ML models can accurately and efficiently predict the ground settlement. Napoved posedanja tal, ki ga povzroča gradnja predorov, je ključnega pomena pri izkopavanju predorov v urbanih območjih. Posedanje tal mora ostati znotraj sprejemljivih mejnih vrednosti, da se preprečijo poškodbe obstoječih stavb in infrastrukture med gradnjo in po njej. Metode strojnega učenja pridobivajo vse večjo priljubljenost na različnih področjih, vključno z gradnjo predorov, saj omogočajo učinkovito učenje in napovedovanje. Prispevek analizira možnosti uporabe različnih metod strojnega učenja za napoved posedanja tal, ki ga povzroča gradnja predorov. Uporabljeni so trije pristopi strojnega učenja: regresija s podporo vektorjev, večplastni perceptron in nevronske mreže dolgega kratkoročnega spomina. Za optimizacijo hiperparametrov sta uporabljeni dve tehniki: optimizacija z rojem delcev in metoda iskanja po mreži. Za oceno učinkovitosti metod strojnega učenja so uporabljene tri metrike: povprečna absolutna napaka, kvadratna srednja napaka in povprečna absolutna odstotkovna napaka. Prispevek prikazuje uporabnost treh metod strojnega učenja za napoved posedanja tal na realnih podatkovnih zbirkah. Eksperimentalni rezultati kažejo, da predlagani modeli strojnega učenja omogočajo natančno in učinkovito napoved posedanja tal posedanje tal gradnja predorov urbana območja strojno učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2026-06-08 16:44:43 2026-06-08 16:44:54 2026-06-09 14:00:50 0000-00-00 00:00:00 2025 0 0 Str. 13-25 št. 1/2 Letn. 92 2025 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.85:69 0013-5852 280328451 RAZ_Serifovic-Trbalic_Amira_2025.pdf RAZ_Serifovic-Trbalic_Amira_2025.pdf 1 1C6330DEAEA4A38556CC8E51B2EA938C a7bb336485f67c453c704d1caeac24880e963e6acab8e7486e7bdca08f3f8efa 9b8f136f-6348-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=235138 https://ev.fe.uni-lj.si/1-2-2025/Trbalic.pdf 0 d8955f02-63fa-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=235200 Fakulteta za elektrotehniko 0 0 0