Lovorka Librić

Primjena rezultata statičkog penetracijskog pokusa za klasifikaciju tla

U radu je prikazana primjena rezultata CPT pokusa za klasifikaciju tla prema Jedinstvenoj klasifikaciji tla (USCS) i Europskoj klasifikaciji tla (ESCS) korištenjem računalnog programa KLASIF. Prikazane su korelacije između rezultata CPT pokusa i parametara za klasifikaciju tla. Na primjeru kanala za navodnjavanje Biđ-Bosutskog polja pokazano je relativno uspješno klasificiranje tla pomoću CPT pokusa.

Application of unmanned aerial vehicles on transport infrastructure network

Ucestale nagle klimatske promjene uzrokuju sve vecu pojavu odrona na prometnoj infrastrukturnoj mreži. Primjenom bespilotnih letjelica mogu se dopuniti, poboljsati, pa cak i u potpunosti zamijeniti klasicni nacini kartiranja, određivanja volumena, poprecnih presjeka, slojnica i drugih parametara koji su potrebni za inženjerske analize odrona. U radu se prikazuju mogucnosti koristenja bespilotnih letjelica u ocjeni stanja prometne infrastrukturne mreže, isticuci njihove prednosti i nedostatke. Prikazana je zakonska regulativa i znanstvenoistraživacke inicijative za primjenu bespilotnih letjelica.

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

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.

Primjena bespilotnih letjelica na prometnoj infrastrukturnoj mreži

Ucestale nagle klimatske promjene uzrokuju sve vecu pojavu odrona na prometnoj infrastrukturnoj mreži. Primjenom bespilotnih letjelica mogu se dopuniti, poboljsati, pa cak i u potpunosti zamijeniti klasicni nacini kartiranja, određivanja volumena, poprecnih presjeka, slojnica i drugih parametara koji su potrebni za inženjerske analize odrona. U radu se prikazuju mogucnosti koristenja bespilotnih letjelica u ocjeni stanja prometne infrastrukturne mreže, isticuci njihove prednosti i nedostatke. Prikazana je zakonska regulativa i znanstvenoistraživacke inicijative za primjenu bespilotnih letjelica.

A Framework for Risk Management in Rockfall Protection

Rockfalls represent a constant threat to the facilities, infrastructure and inhabitants. Therefore, it is necessary to map the rockfall hazard area and to carry out the risk assessment. In the Republic of Croatia there is no systematic data acquisition of rockfalls. There is also no rockfall classification system adjusted for Croatian Karst. In this paper a Framework for Risk Management in Rockfall Protection in Croatian Karst is presented, which enables a better understanding of the problem and the cooperation of all participants in the project. Decision-making process, necessary for complex decisions, depends on the systematic study of influential factors, the adequacy and quality of information, the number of alternatives, and the use of appropriate models and techniques for selecting optimal alternative. Quantitative risk analysis is carried out if there is a possibility of estimating the probability of a certain event, based on available information about similar events that occurred in the past or on information collected in any other way and also based on personal experience. In the absence of such experience proposed framework allows a qualitative risk analysis with constant control of the consistency of subjective decision-making.