Mario Bacic

Dr. Mario Bacic

Department of Geotechnics, Faculty of Civil Engineering, University of Zagreb, Zagreb, Croatia
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Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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9 March 2021 in Water
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Journal Article
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NRNicola Rossi
Nicola Rossi
Mario BacicMario Bacic
Mario Bacic
Meho Sasa KovacevicMeho Sasa Kovacevic
Meho Sasa Kovacevic
Lovorka LibricLovorka Libric
Lovorka Libric
The design code Eurocode 7 relies on semi-probabilistic calculation procedures, through utilization of the soil parameters obtained by in situ and laboratory tests, or by the means of transformation models. To reach a prescribed safety margin, the inherent soil parameter variability is accounted for through the application of partial factors to either soil parameters directly or to the resistance. However, considering several sources of geotechnical uncertainty, including the inherent soil variability, measurement error and transformation uncertainty, full probabilistic analyses should be implemented to directly consider the site-specific variability. This paper presents the procedure of developing fragility curves for levee slope stability and piping as failure mechanisms that lead to larger breaches, where a direct influence of the flood event intensity on the probability of failure is calculated. A range of fragility curve sets is presented, considering the variability of levee material properties and varying durations of the flood event, thus providing crucial insight into the vulnerability of the levee exposed to rising water levels. The procedure is applied to the River Drava levee, a site which has shown a continuous trend of increased water levels in recent years.

8 January 2020 in Materials
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Mario BacicMario Bacic
Mario Bacic
Meho Sasa KovacevicMeho Sasa Kovacevic
Meho Sasa Kovacevic
DJDanijela Jurić-Kaćunić
Danijela Jurić-Kaćunić
Grouted rock bolts represent one of the most used elements for rock mass stabilization and reinforcement and the grouting quality has a crucial role in the load transfer mechanism. At the same time, the grouting quality as well as the grouting procedures are the least controlled in practice. This paper deals with the non-destructive investigation of grouting percentage through an analysis of the rock bolt’s natural frequencies after applying an artificial longitudinal impulse to its head by using a soft-steel hammer as a generator. A series of laboratory models, with different positions and percentages of the grouted section, simulating grouting defects, were tested. A comprehensive statistical analysis was conducted and a high correlation between the grouting percentage and the first three natural frequencies of rock bolt models has been established. After validation of FEM numerical models based on experimentally obtained values, a further analysis includes consideration of grout stiffness variation and its impact on natural frequencies of rock bolt.

11 October 2020 in Geosciences
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Mario BacicMario Bacic
Mario Bacic
Lovorka LibricLovorka Libric
Lovorka Libric
DJDanijela Jurić-Kaćunić
Danijela Jurić-Kaćunić
Meho Sasa KovacevicMeho Sasa Kovacevic
Meho Sasa Kovacevic
Having in mind uncertainties linked with the nature of rock masses, it is a challenge for geotechnical engineers to characterize them in a credible manner, especially if the rock mass in question is a notoriously complex karst with its distinctive features such as voids and discontinuities. A large range of geophysical methods are useful tools in the characterization efforts and their utilization in karst environments nowadays is a non-negotiable requirement, even though near-surface scattering significantly affects the acquired data. This paper demonstrates the versatile applications of seismic geophysical methods for geotechnical engineering in karst, from mapping of near-surface karstic features to the application of elastic wave velocities in determination of small to large strain stiffness of karst. The highlighted practical examples offer a step forward from the traditional interpretation of seismic surveys, making them a prosperous tool in geotechnical engineering investigation works, design and quality control campaign.

1 April 2021 in Canadian Geotechnical Journal
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Journal Article
Closed access
MKMeho Saša Kovačević
Meho Saša Kovačević
Mario BacicMario Bacic
Mario Bacic
KGKenneth Gavin
Kenneth Gavin
This paper offers a solution to overcome time-consuming numerical analysis for the evaluation of the impact of tunnel construction in a complex karst environment by implementing Monte Carlo Simulation (MCS) using a neural network (NN) tool. The rock mass is described using three parameters: Geological Strength Index, the uniaxial compression strength of the intact rock, and the Hoek–Brown parameter for the intact rock m i. By using their probabilistic distribution as an input, a developed neural network NetTUNN produces probabilistic distributions of tunnel crown displacement, rock bolt axial load, and shotcrete uniaxial compression stress. A full MCS is then applied on these NetTUNN outputs to determine the reliability index and probability of failure for the relevant limit states. To demonstrate the potential of NN in tunnel design, a case study of Tunnel Pećine in Croatia is used, where the NetTUNN-assisted MCS assessment served as a benchmark to evaluate approximate reliability assessment techniques. It was shown that the developed NN can be used as an accurate surrogate model for determination of probabilistic distributions of tunnel design parameters. Further, it was shown that approximate reliability assessment techniques generally overestimate the reliability index and underestimate the probability of failure when compared to the NetTUNN-assisted MCS.

9 April 2022 in Sensors
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Meho Sasa KovacevicMeho Sasa Kovacevic
Meho Sasa Kovacevic
Mario BacicMario Bacic
Mario Bacic
Lovorka LibricLovorka Libric
Lovorka Libric
Kennet GavinKennet Gavin
Kennet Gavin
To identify the unknown values of the parameters of Burger’s constitutive law, commonly used for the evaluation of the creep behavior of the soft soils, this paper demonstrates a procedure relying on the data obtained from multiple sensors, where each sensor is used to its best advantage. The geophysical, geotechnical, and unmanned aerial vehicle data are used for the development of a numerical model whose results feed into the custom-architecture neural network, which then provides information about on the complex relationships between the creep characteristics and soil displacements. By utilizing InSAR and GPS monitoring data, particle swarm algorithm identifies the most probable set of Burger’s creep parameters, eventually providing a reliable estimation of the long-term behavior of soft soils. The validation of methodology is conducted for the Oostmolendijk embankment in the Netherlands, constructed on the soft clay and peat layers. The validation results show that the application of the proposed methodology, which relies on multisensor data, can overcome the high cost and long duration issues of laboratory tests for the determination of the creep parameters and can provide reliable estimates of the long-term behavior of geotechnical structures constructed on soft soils.

25 November 2019 in Applied Sciences
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Meho Sasa KovacevicMeho Sasa Kovacevic
Meho Sasa Kovacevic
Mario BacicMario Bacic
Mario Bacic
ISIrina Stipanović
Irina Stipanović
Kennet GavinKennet Gavin
Kennet Gavin
In the current economic climate, it is crucial to optimize the use of all resources regarding railway infrastructure maintenance. In this paper, a multi-attribute decision support framework is applied to categorize railway embankments in order to prioritize maintenance activities. The paper describes a methodology to first determine the current condition of embankments using a combination of ground penetrating radar (GPR) surveys, visual inspection, and historical data about maintenance activities. These attributes are then used for the development of a multi-attribute utility theory model, which can be used as a support for decision making process for maintenance planning. The methodology is demonstrated for the categorization of 181 km of railway embankments in Croatia.