Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 974577

Automatic classification of fine-grained soils using CPT measurements and Artificial Neural Networks


Reale, Cormac; Gavin, Kenneth; Librić, Lovorka; Jurić-Kaćunić, Danijela
Automatic classification of fine-grained soils using CPT measurements and Artificial Neural Networks // Advanced Engineering Informatics, 36 (2018), 207-215 doi:10.1016/j.aei.2018.04.003 (međunarodna recenzija, članak, ostalo)


Naslov
Automatic classification of fine-grained soils using CPT measurements and Artificial Neural Networks

Autori
Reale, Cormac ; Gavin, Kenneth ; Librić, Lovorka ; Jurić-Kaćunić, Danijela

Izvornik
Advanced Engineering Informatics (1474-0346) 36 (2018); 207-215

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, ostalo

Ključne riječi
CPT Soil classification Machine learning ANN Neural networks

Sažetak
Soil classification is a means of grouping soils into categories according to a shared set of properties or characteristics that will exhibit similar engineering behaviour under loading. Correctly classifying site conditions is an important, costly, and time-consuming process which needs to be carried out at every building site prior to the commencement of construction or the design of foundation systems. This paper presents a means of automating classification for fine-grained soils, using a feed-forward ANN (Artificial Neural Networks) and CPT (Cone Penetration Test) measurements. Thus representing a significant saving of both time and money streamlining the construction process. 216 pairs of laboratory results and CPT tests were gathered from five locations across Northern Croatia and were used to train, test, and validate the ANN models. The resultant Neural Networks were saved and were subjected to a further external verification using CPT data from the Veliki vrh landslide. A test site, which the model had not previously been exposed to. The neural network approach proved extremely adept at predicting both ESCS (European Soil Classification System) and USCS (Unified Soil Classification System) soil classifications, correctly classifying almost 90% of soils. While the soils that were incorrectly classified were only partially misclassified. The model was compared to a previously published model, which was compiled using accepted industry standard soil parameter correlations and was shown to be a substantial improvement, in terms of correlation coefficient, absolute average error, and the accuracy of soil classification according to both USCS and ESCS guidelines. The study confirms the functional link between CPT results, the percentage of fine particles FC, the liquid limit wL and the plasticity index IP. As the training database grows in size, the approach should make soil classification cheaper, faster and less labour intensive.

Izvorni jezik
Engleski

Znanstvena područja
Građevinarstvo



POVEZANOST RADA


Ustanove
Građevinski fakultet, Zagreb

Citiraj ovu publikaciju

Reale, Cormac; Gavin, Kenneth; Librić, Lovorka; Jurić-Kaćunić, Danijela
Automatic classification of fine-grained soils using CPT measurements and Artificial Neural Networks // Advanced Engineering Informatics, 36 (2018), 207-215 doi:10.1016/j.aei.2018.04.003 (međunarodna recenzija, članak, ostalo)
Reale, C., Gavin, K., Librić, L. & Jurić-Kaćunić, D. (2018) Automatic classification of fine-grained soils using CPT measurements and Artificial Neural Networks. Advanced Engineering Informatics, 36, 207-215 doi:10.1016/j.aei.2018.04.003.
@article{article, year = {2018}, pages = {207-215}, DOI = {10.1016/j.aei.2018.04.003}, keywords = {CPT Soil classification Machine learning ANN Neural networks}, journal = {Advanced Engineering Informatics}, doi = {10.1016/j.aei.2018.04.003}, volume = {36}, issn = {1474-0346}, title = {Automatic classification of fine-grained soils using CPT measurements and Artificial Neural Networks}, keyword = {CPT Soil classification Machine learning ANN Neural networks} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
  • Scopus


Citati