Optimization techniques for identifying soil parameters in geotechnical engineering: Comparative study and enhancement

Introduction:Reliability analysis is a good tool to deal with the uncertainty and has been widely used in the engineering system. The first order reliability method (FORM) is generally used to calculate the reliability index but FORM is time-consuming and requires derivative computing.Methods:Artificial Bee Colony (ABC) algorithm is a very simple, robust and population-based stochastic optimization algorithm. In this study, an ABC-based reliability analysis was proposed to calculate the reliability index of engineering system through combining Artificial Bee Colony (ABC) algorithm with FORM. FORM was adopted to calculate the reliability index and design point. ABC is used to solve the constrained optimization about FORM. The procedure of ABC-based reliability analysis was presented in detail.Results and Conclusion:The proposed method was verified by two classic examples and then applied to geotechnical engineering. The results show that the ABC algorithm can effectively solve the global optimization problem in FORM. Results demonstrate that ABC-based reliability analysis is a good approach to obtain the reliability index and design point with a good accuracy so that it can be applied to analyze the reliability of a complex engineering system.

The hydraulic conductivity is a key parameter in geotechnical engineering. It is closely associated to soil microstructure. In practice, consolidation stress, dry density, and natural structure of soil can affect pore diameter and distribution of soil. Thus, to investigate soil hydraulic conductivity, their effects on microstructure and the corresponding hydraulic conductivity were analyzed. The undisturbed soil and the remolded soil with different dry densities were studied by the oedometer tests, hydraulic conductivity tests, and field emission scanning electron microscope (FESEM) tests. The results indicate that the natural structure inside the undisturbed soil is the intrinsic reason that affects hydraulic conductivity. The increase of consolidation stress mainly reduces the number and size of interaggregate pores in terms of provoking aggregation of soil particles. The increase in dry density will fill the interaggregate pores and weaken their connectivity. The area percentage of macropores can be used to reflect the change of soil microstructure. The consolidation stress is the main factor dominating the area percentage of macropores under a high-stress level. A stable intrinsic structure will be developed to inhibit the soil deformation when dry density is 1.4 g/cm3 or above, which helps to restrain the reduction of area percentage and slow down the decrease of hydraulic conductivity. Finally, a brief statistical regression analysis indicates that the area percentage of pores whose diameter larger than 0.4 μm is significantly related to the hydraulic conductivity, which are responsible for seepage in soft clay.

The thermal conductivity of soil is a fundamental physical property related to heat conduction and also one of the widely applied parameters in geotechnical engineering. Thermal needle tests were performed to measure the thermal conductivity of transparent sand. The thermal conductivity of three particle sizes of transparent sand was measured at different porosities and temperatures. The result shows that the thermal conductivity of transparent sand decreases with the increase of porosity and temperature. A modified model is proposed to calculate the thermal conductivity of the tested sand. Compared with the literature models, which are only suitable for room temperature, the modified model in this paper is applicable to a wider temperature range, ranging from 5∘C to 45∘C. It will also be beneficial to future studies on thermal reponses of granular materials, i.e., energy pile model test using transparent sand.

The electrical conductivity (EC) of soils is a crucial parameter in geotechnical engineering, providing valuable insights into soil properties such as mineralogy, moisture content, and particle distribution. This study explores the electrical conductivity of fine-grained soils and its application in soil classification. A series-parallel structure-oriented model for electrical conductivity is proposed, incorporating parameters such as diffuse double layer (DDL) effects, pore water salinity, and temperature influence. Experimental validation confirms that EC measurements can accurately predict soil classification attributes such as particle size distribution (PSD), liquid limit (LL), and plastic limit (PL). The proposed approach provides a cost-effective and rapid alternative to conventional geotechnical testing methods.

Uniaxial compressive strength and undrained shear strength are among the most important soil parameters in geotechnical engineering. The unconsolidated undrained triaxial test and the falling cone test determine them. The uniaxial compressive strength of soil is the maximum compressive stress that the soil can withstand before failure in drained conditions. In contrast, the undrained shear strength of the soil represents the maximum shear stress that the soil can withstand before failure in undrained conditions. In this paper, using modular clay as an example, the results of laboratory tests that determine these parameters according to Croatian standards are presented and compared.

Steel slag has similar characteristics with sand, and pure steel slag used as geotechnical backfill material is an effective way to improve the comprehensive utilization rate of waste steel slag. In order to study characteristics of pure steel slag for application in geotechnical engineering, the compressive deformation characteristics and shear strength characteristics of steel slag under different water contents were explored by unidirectional consolidation compression tests and direct shear tests. Change rules of densities, compressive moduli, cohesion and internal friction angles of steel slag under different water contents were analyzed. It can be concluded that the compressive modulus of steel slag decreased with increasing the water content. The compressive modulus of dry steel slag was larger, reaching the level of medium sand. Compression moduli at different water contents were higher than those of silt soil and soft clay. Steel slag can be used as backfill material instead of mud soil and soft clay in the treatment of soft soil foundation. The cohesion of steel slag increased and the internal friction angle decreased with increasing the water content. The shear strength of dry steel slag was similar to that of medium sand, indicating that steel slag was more suitable as roadbed filling above the groundwater level.

Gbest-guided artificial bee colony algorithm for numerical function optimization

A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system

This paper presents the results of the nonlinear system modelling approach based on the use of fuzzy rules optimized by different population based optimization algorithms. Fuzzy rule based models with different number of the rules are used to describe the some nonlinear systems in the literature. Firstly, parameters of the fuzzy models are determined by the artificial bee colony (ABC) algorithm. To demonstrate the efficiency of the ABC algorithm, its modelling ability is compared with the other two powerful population based algorithms, particle swarm optimization (PSO) and differential evolution algorithm (DEA). Simulation results show that a successful model performance with good description ability in the modelling of nonlinear or complex systems can be obtained by using one of the population based algorithms in design of the fuzzy rule based models. DOI: http://dx.doi.org/10.5755/j01.eie.22.5.16336

A differential evolution algorithm with intersect mutation operator

ABSTRACTInverse synthetic aperture radar (ISAR) imaging is an efficient technique to generate two‐dimensional spatial distributions of radar cross‐section of a target. The received signal of ISAR contains interphase errors resulting in blurring problem in the images due to motion effects of the target. In this study, a simple, stable, and robust method based on artificial bee colony (ABC) algorithm has been proposed for motion compensation (MoComp) to overcome the blurring problem in ISAR images. The minimum entropy was used as objective function in the ABC algorithm for optimally determining the parameters of velocity and acceleration. The proposed method was implemented to four different scenarios based on the two air target models to show performance of the ABC algorithm. The radar signal of the target without MoComp was given as inputs to the ABC algorithm to determine velocity and acceleration values so as to provide the clearest ISAR image. The proposed method was compared with a suggestion reported elsewhere using particle swarm optimization (PSO), PSO with island model, genetic algorithm. It is pointed out that the proposed method is more successful, stable and efficient in terms of the image quality, entropy values, and standard deviation. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:2691–2698, 2014

The maximum power point tracking (MPPT) system ensures that the solar cell operates at its maximum power point, which is the point at which it produces the most power for a given set of conditions. This study uses an artificial bee colony (ABC) algorithm and particle swarm optimization (PSO) algorithms to optimize the boost converter. The ABC and PSO algorithms help to optimize the boost converter by finding the best possible duty cycle and other parameters that maximize efficiency. The resulting duty cycle of 20% produced the highest efficiency of 78.26% obtained from the boost converter. A system-wide test was conducted between 9:00 and 11:10 WIB. In MPPT systems using boost converters with the PSO algorithm, the average power is 16.80 W, while in MPPT systems using boost converters with the ABC algorithm, the average power is 14.53 W. The novelty of the study discusses a system designed to optimize power by using a boost converter in order to increase power output from solar panels. The algorithms PSO and ABC are used to analyze the data from the PV and adjust the converter parameters to optimize the output in order to increase the efficiency of the system.

The continuous artificial bee colony algorithm for binary optimization

In this paper, Artificial Bee Colony (ABC) algorithm which inspired from the behavior of honey bees swarm is presented. ABC is a stochastic population-based evolutionary algorithm for problem solving. ABC algorithm, which is considered one of the most recently swarm intelligent techniques, is proposed to optimize least square support vector machine (LSSVM) to predict the daily stock prices. The proposed model is based on the study of stocks historical data, technical indicators and optimizing LSSVM with ABC algorithm. ABC selects best free parameters combination for LSSVM to avoid over-fitting and local minima problems and improve prediction accuracy. LSSVM optimized by Particle swarm optimization (PSO) algorithm, LSSVM, and ANN techniques are used for comparison with proposed model. Proposed model tested with twenty datasets representing different sectors in S&P 500 stock market. Results presented in this paper show that the proposed model has fast convergence speed, and it also achieves better accuracy than compared techniques in most cases.

Geotextiles, a group of high-performance materials, have grown during the last decades into needful auxiliaries when it comes to infrastructure, soil, construction, agriculture and environmental applications. Although geotextiles made of synthetic fibers (geosynthetics) are considered a modern achievement, the basic concept dates back to ancient times when textiles consisting of locally available natural fibers were employed to increase the stability of roads and soils. In recent decades, considering the growing interest in environmental protection and sustainable development based on using renewable resources and the recovery and recycling of waste of various origins, the use of natural fibers-based geotextiles is a viable alternative, despite their limited-life service owing to their biodegradability. In addition to this feature, their low cost, good mechanical properties and large-scale accessibility recommend them for geo-engineering applications, environmental sensitive applications in geotechnical engineering, such as land improvements and soil erosion control. This paper focuses on geotextiles as a versatile tool in environmental applications given their high theoretic and practical relevance as substantiated by recent literature reports. Natural and synthetic geotextiles are presented herein, as well as their features that recommend them for geo-engineering. Insights on the main types of applications of geotextiles are also included, along with a wide variety of materials employed to perform specific functions.