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 w L and the plasticity index I P. As the training database grows in size, the approach should make soil classification cheaper, faster and less labour intensive.

Kaćunić: Raziskave in remediacija jamske dvorane v predoru Vrata na avtocesti Zagreb-Reka (Hrvaška) V hrvaškem delu Dinarskega krasa je raziskanih preko 11500 jam, od teh je bilo več kot tisoč odkritih med gradbenimi deli. Večino teh, ki sicer nimajo naravnega vhoda s površja, so odkrili med gradnjo avtocest. Te jame sistematični raziskujemo zadnjih dvajset let. V članku predstavimo več izvirnih tehničnih rešitev, ki so jih izvedli za ohranitev velike podzemne dvorane, odkrite med gradnjo predora Vrata na avtocesti Zagreb Reka. Dvorana, ki jo seka predor, je zaradi velikosti in hidrogeološkega pomena predstavljala velik geotehnični izziv. Preko dvorane so speljali 58 m dolg most, poleg tega pa so morali ojačiti in stabilizirati svod, ker je debelina skladov nad dvorano majhna. V ta namen so izdelali mrežo nosilcev in gredi, ki so jo učvrstili s posebnimi pritrdišči. Most, ki je brez podpornih stebrov, je najdaljši most v jami na svetu.

No abstract