Articles from journal Acta Geotechnica Slovenica 2022/2
The resilient modulus of hybrid construction and demolition wastes reinforced by a geogrid
Talha Sarici, Bahadir Ok, Aykan Mert, Senol Comez
The use of construction and demolition wastes (C&D) in engineering applications is an important development for better sustainability. The main objective of this study, therefore, was to increase the use of C&D by improving their engineering behaviour. For this purpose, two methods were employed in this study: first, adding the virgin aggregates (VA) to the C&D, called hybrid C&D (C&D-VA), and second, reinforcing the C&D with a geogrid material. Test samples were prepared in six groups. The first three test groups were prepared with C&D, VA and C&D-VA. The other three test groups were formed with geogrid-reinforced C&D, VA and C&D-VA. Firstly, for the strength characteristics of the samples, the unconfined compressive strength and the California bearing ... ratio values were obtained with large-scale experiments. Subsequently, for the resilient behaviour of the samples, the resilient modulus values were determined using a large-scale triaxial test device. Consequently, some significant improvements were achieved via the methods employed in this study. In addition, it was observed that the best reinforcement effect for the C&D occurred when the geogrid was used and the VA was added to the C&D.
Keywords: construction and demolition waste Geogrid geotechnical engineering sustainability resilient modulus waste management
Geotechnical characterization of zeolite-sand and bentonite-sand mixtures
Özgür Yildiz, Çiğdem Ceylan
This paper presents the characterization of pure bentonite- and zeolite-type clays and of various contents mixed with sand. The engineering properties of zeolites, bentonites and sand, which are commonly found in Malatya, Turkey, were evaluated in terms of their suitability for geotechnical applications. The crystallinity and structure of solid specimens of bentonite and zeolite were analysed with X-ray diffraction. Then both soils were mixed with sand in various proportions and the enhancement of the engineering properties was investigated. The properties of the mixtures, such as specific gravity, optimum water content, and ... dry unit weight mixtures, were initially determined. A set of direct shear tests was carried out to determine the shear-strength parameters of the specimens. As a result of extensive laboratory tests, linear correlations were observed between the water content and the consistency limits with the bentonite and zeolite contents in the sand mixtures. The highest for among each sample tested was achieved with the addition of 50 % bentonite and zeolite (i.e., BS50 and ZS50) as 44 and 38 kPa, respectively. A literature survey was carried out to reveal the test results of similar studies. In addition, using the test results from these literature studies and the current study, an NN-based prediction model was developed. The forecast models developed separately for cohesion and internal friction angle had high correlation coefficients: R2 equal to 0.84 for cohesion and R2 equal to 0.78 for the friction angle.
Keywords: zeolite bentonite shear strength correlation neural networks prediction
SPT-based soil-liquefaction models using nonlinear regression analysis and artificial intelligence techniques
Mehmet Cemal Acar, Tülay Hakan
Saturated, cohesionless soils can temporarily lose their shear strength due to increased pore-water pressure under the effect of repetitive dynamic loads such as earthquakes. This event is defined as soil liquefaction and causes significant damage to structures. The liquefaction potential of soils depends on many soil parameters obtained in the field and from laboratory tests. In this study new models have been developed to estimate the liquefaction potential of cohesionless soils. For this purpose, 837 soil data sets were collected to calculate the liquefaction potential with nonlinear multiple regression and ... artificial intelligence in the cities of Kayseri and Erzincan. The models based on Nonlinear Multiple Regression Analysis, Artificial Neural Networks, and Adaptive Neuro-Fuzzy-Inference System techniques were compared with the results of the simplified method. Determination coefficients (R2) and various error rates were calculated for the performance-evaluation criteria of the models. The proposed ANN model effectively found the complex relationship between the soil and the input parameters and predicts the liquefaction potential more accurately than other methods. It has an overall success rate of 90 percent and the lowest mean absolute error rate of 0.024. With the improvement of existing methods, new models have been introduced to estimate the liquefaction probability of soils.
Keywords: liquefaction standard penetration test (SPT) ANN ANFIS NMRA
SCIE - Science Citation Index Expanded, JCR – Journal Citation Reports / Science Edition, ICONDA - The international Construction database, GeoRef
ACTA GEOTECHNICA SLOVENICA aims to play an important role in publishing high-quality, theoretical papers from important and emerging areas that will have a lasting impact on fundamental and practical aspects of geomechanics and geotechnical engineering. It publishes papers from the following areas: soil and rock mechanics, engineering geology, environmental geotechnics, geosynthetic, geotechnical structures, numerical and analytical methods, computer modelling, optimization of geotechnical structures, field and laboratory testing.