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In this paper a heuristic approach for preliminary regional landslide susceptibility assessment using limited amount of data is presented. It is called arbitrary polynomial method and takes into account 5 landslide conditioning parameters: lithology, slope inclination, average annual rainfall, land use and maximum expected seismic intensity. According to the method, in the first stage, a gradation is performed for each of the carefully selected conditioning parameters by assigning so called rating points to the grid cells on which the region is divided. Values of the rating points vary from 0 to 3 and depend on the parameter’s character and importance for landslide development within the region of interest. A so called Total Landslide Susceptibility Rating (TLSR) model is obtained by summing the individual rating points of each parameter and dividing the region into five susceptibility zones according to Jenks natural breaks classification. Verification of the TLSR model is then performed by overlaying the landslide inventory map of the selected region over the prepared susceptibility map. The sensitivity of the model can be additionally tested by multiplying the conditioning parameter’s rating points by sensitivity coefficients. In this way, additional landslide susceptibility models are obtained, named Weighted Total Landslide Susceptibility Rating (WTLSR) models. As a practical example of the method, two TLSR models are presented here for the Polog region in Republic of Macedonia, for return periods of maximum expected seismic intensity for 100 and 500 years. With over 74% of mapped landslides falling in zones of high and very high susceptibility, the results are considered satisfactory for regional scale landslide modelling and are comparable with more advanced quantitative methods. Additional WTLSR models were prepared, and their correlation identified the best model. The presented approach is considered to be very convenient for conducting preliminary regional

In this paper a heuristic approach for preliminary regional landslide susceptibility assessment using limited amount of data is presented. It is called arbitrary polynomial method and takes into account 5 landslide conditioning parameters: lithology, slope inclination, average annual rainfall, land use and maximum expected seismic intensity. According to the method, in the first stage, a gradation is performed for each of the carefully selected conditioning parameters by assigning so called rating points to the grid cells on which the region is divided. Values of the rating points vary from 0 to 3 and depend on the parameter’s character and importance for landslide development within the region of interest. A so called Total Landslide Susceptibility Rating (TLSR) model is obtained by summing the individual rating points of each parameter and dividing the region into five susceptibility zones according to Jenks natural breaks classification. Verification of the TLSR model is then performed by overlaying the landslide inventory map of the selected region over the prepared susceptibility map. The sensitivity of the model can be additionally tested by multiplying the conditioning parameter’s rating points by sensitivity coefficients. In this way, additional landslide susceptibility models are obtained, named Weighted Total Landslide Susceptibility Rating (WTLSR) models. As a practical example of the method, two TLSR models are presented here for the Polog region in Republic of Macedonia, for return periods of maximum expected seismic intensity for 100 and 500 years. With over 74% of mapped landslides falling in zones of high and very high susceptibility, the results are considered satisfactory for regional scale landslide modelling and are comparable with more advanced quantitative methods. Additional WTLSR models were prepared, and their correlation identified the best model. The presented approach is considered to be very convenient for conducting preliminary regional

U radu je prikazano ocjenjivanje metoda za predviđanje granične osne nosivosti pojedinačnih pilota na bazi rezultata statičkog penetracijskog pokusa (CPT), te ocjenjivanje statičkih metoda baziranih na analizi efektivnog i ukupnog naprezanja. Analizirano je sedamnaest utisnutih MEGA pilota i osam Franki pilota različitih dužina. Dobiveni rezultati pokazuju da je, među izravnim CPT metodama, za analizirane geotehničke uvjete i tipove pilota najbolja metoda koju predlažu Bustamante i Gianeselli (LCPC)., This paper presents the evaluation of methods for predicting the ultimate axial bearing capacity of single piles based on the cone penetration test (CPT) results, and the evaluation of static methods based on the effective and total stress analysis. Seventeen jacked-in MEGA piles and eight Franki piles of different lengths are considered. The results show that the Bustamante & Gianeselli (LCPC) method is the best direct CPT method, while also being the most appropriate for the studied geotechnical conditions and pile types.

Landslide Susceptibility Assessment is becoming a very productive research area, wherein different modeling approaches are practiced to delineate zones of the high-low likelihood of landslide occurrence. However, there is no strong consensus on which approach is the most adequate. The reason behind the lack of the general view on the performance of different approaches could be partially explained by the particularity of each study. To evaluate the efficiency of different approaches they need to be applied under the same conditions for the same study area. Herein, we examined three different approaches, in- cluding expert, deterministic and Machine Learning, on the example of Ljubovija Municipality in western Serbia. The study area has been known as susceptible to landslides, and represents good ground for assessing the chosen methods. It is represented by complex geology, prone to landslides that are commonly hosted in thick weathering crust of Paleozoic formations, composed of schists and meta-sediments. Under extreme triggering conditions, such as the one that unfolded in May 2014, these thick weathering crusts saturate, and give way to a variety of landslide and flash-flood processes that we will be focusing on in this study. The application of the expert-approach, through Analytical Hierarchy Process provided a rough assessment map. The deterministic model, which couples simple infinite slope and hydrological model, provided us with lower quality results, when compared to the expert-based one. This could be explained by the assumptions used in the model are too simplistic to generically model a wide range of landslide typology. Finally, Machine Learning approach, using the Random Forest algorithm, provided significantly better results and showed that it can cope with versatile landslide typology over larger scales. Its AUC performance is about 0.75 which is considerably outperforming the AUC values of the other two models, which were up to 0.55, i.e. at the lev, Procjena osjetljivosti na klizišta postaje vrlo produktivno istraživačko područje, pri čemu se prakticiraju različiti pristupi modeliranju kako bi se zonirale visoke i niske vjerojatnosti pojave klizišta. Međutim, ne postoji jasna suglasnost o tome koji je pristup najprikladniji. Razlog nedostatka općeg gledišta na izvedbu različitih pristupa mogao bi se djelomično objasniti osobitostima svake studije. Za procjenu učinkovitosti različitih pristupa neophodno je primjenjivati ih pod istim uvjetima za isto područje istraživanja. U ovome radu su istraživana tri različita pristupa, uključujući ekspertni, deterministički i pristup strojnog učenja, na području općine Ljubovija u zapadnoj Srbiji. Područje je poznato kao osjetljivo na klizišta i predstavlja dobru osnovu za procjenu odabranih metoda. Odlikuje ga kompleksna geologija i sklonost pojavi klizišta koja se obično nalaze u debeloj kori raspadanja paleozojskih formacija sastavljenih od škriljaca i meta-sedimenata. Pod ekstremnim uvjetima za aktivaciju, poput onog koji se odvijao u svibnju 2014., ove debele kore raspadanja se zasićuju i omogućuju raznovrsne pojave klizišta i bujica, na koje ćemo se usredotočiti u ovoj studiji. Primjena ekspertnog pristupa kroz analitički hijerarhijski proces je dala grubu kartu procjene. Deterministički model, koji integrira model beskonačne kosine i hidrološki model, dao je lošije rezultate u usporedbi sa ekspertnom metodom. Ovo se može objasniti time da su pretpostavke korištene u modelu bile previše jednostavne da generički modeliraju takav široki raspon tipologije klizišta. Konačno, pristup strojnog učenja, korištenjem algoritma Random Forest, dao je znatno bolje rezultate i pokazalo se da se može uspješno koristiti s raznovrsnom tipologijom klizišta na većoj prostornoj skali. Njegov AUC učinak je oko 0,75 , što je znatno bolje od AUC vrijednosti druga dva modela koji su do 0,55 , tj. na razini slučajnog nagađanja.

Landslide Susceptibility Assessment is becoming a very productive research area, wherein different modeling approaches are practiced to delineate zones of the high-low likelihood of landslide occurrence. However, there is no strong consensus on which approach is the most adequate. The reason behind the lack of the general view on the performance of different approaches could be partially explained by the particularity of each study. To evaluate the efficiency of different approaches they need to be applied under the same conditions for the same study area. Herein, we examined three different approaches, in- cluding expert, deterministic and Machine Learning, on the example of Ljubovija Municipality in western Serbia. The study area has been known as susceptible to landslides, and represents good ground for assessing the chosen methods. It is represented by complex geology, prone to landslides that are commonly hosted in thick weathering crust of Paleozoic formations, composed of schists and meta-sediments. Under extreme triggering conditions, such as the one that unfolded in May 2014, these thick weathering crusts saturate, and give way to a variety of landslide and flash-flood processes that we will be focusing on in this study. The application of the expert-approach, through Analytical Hierarchy Process provided a rough assessment map. The deterministic model, which couples simple infinite slope and hydrological model, provided us with lower quality results, when compared to the expert-based one. This could be explained by the assumptions used in the model are too simplistic to generically model a wide range of landslide typology. Finally, Machine Learning approach, using the Random Forest algorithm, provided significantly better results and showed that it can cope with versatile landslide typology over larger scales. Its AUC performance is about 0.75 which is considerably outperforming the AUC values of the other two models, which were up to 0.55, i.e. at the lev, Procjena osjetljivosti na klizišta postaje vrlo produktivno istraživačko područje, pri čemu se prakticiraju različiti pristupi modeliranju kako bi se zonirale visoke i niske vjerojatnosti pojave klizišta. Međutim, ne postoji jasna suglasnost o tome koji je pristup najprikladniji. Razlog nedostatka općeg gledišta na izvedbu različitih pristupa mogao bi se djelomično objasniti osobitostima svake studije. Za procjenu učinkovitosti različitih pristupa neophodno je primjenjivati ih pod istim uvjetima za isto područje istraživanja. U ovome radu su istraživana tri različita pristupa, uključujući ekspertni, deterministički i pristup strojnog učenja, na području općine Ljubovija u zapadnoj Srbiji. Područje je poznato kao osjetljivo na klizišta i predstavlja dobru osnovu za procjenu odabranih metoda. Odlikuje ga kompleksna geologija i sklonost pojavi klizišta koja se obično nalaze u debeloj kori raspadanja paleozojskih formacija sastavljenih od škriljaca i meta-sedimenata. Pod ekstremnim uvjetima za aktivaciju, poput onog koji se odvijao u svibnju 2014., ove debele kore raspadanja se zasićuju i omogućuju raznovrsne pojave klizišta i bujica, na koje ćemo se usredotočiti u ovoj studiji. Primjena ekspertnog pristupa kroz analitički hijerarhijski proces je dala grubu kartu procjene. Deterministički model, koji integrira model beskonačne kosine i hidrološki model, dao je lošije rezultate u usporedbi sa ekspertnom metodom. Ovo se može objasniti time da su pretpostavke korištene u modelu bile previše jednostavne da generički modeliraju takav široki raspon tipologije klizišta. Konačno, pristup strojnog učenja, korištenjem algoritma Random Forest, dao je znatno bolje rezultate i pokazalo se da se može uspješno koristiti s raznovrsnom tipologijom klizišta na većoj prostornoj skali. Njegov AUC učinak je oko 0,75 , što je znatno bolje od AUC vrijednosti druga dva modela koji su do 0,55 , tj. na razini slučajnog nagađanja.

U radu su prikazani rezultati laboratorijskog ispitivanja stabilizacije tla letećim pepelom bez primjene aktivatora. Ispitivane su dvije vrste sitnozrnatog tla: glina niske do srednje plastičnosti te vrlo ekspanzivna glina srednje do visoke plastičnosti. Mješavine tla i letećeg pepela su pripremljene s optimalnom količinom pepela (15 i 20 %). Ispitivan je utjecaj letećeg pepela na plastičnost tla, odnos vlage i gustoće, jednoosnu tlačnu čvrstoću, vrijednosti parametara posmične čvrstoće, vrijednosti CBR-a (California Bearing Ratio-CBR), deformacije i potencijal bujanja. Dobiveni rezultati pokazuju da primjena letećeg pepela može značajno poboljšati svojstva tla., Results of laboratory research focusing on soil stabilization, using fly ash without activators, are presented in the paper. Two types of fine-grained soils were tested: low to medium plasticity clay and very expansive, medium to high plasticity clay. Soil-fly ash mixtures were prepared at optimum fly ash contents (15 and 20 %). The effects of fly ash on the soil plasticity, moisture-density relationship, unconfined compressive strength, shear strength parameters, CBR (California Bearing Ratio) values, deformation parameters, and swell potential, were evaluated. Results obtained show that the use uf fly ash can significantly contribute to the improvement of soil properties.