• Structural Engineering and Mechanics
• /
• v.62 no.3
• /
• pp.259-272
• /
• 2017

In this study, recently developed swarm intelligence algorithm called Social Spider Optimization (SSO) approach and its enhanced version of SSO algorithm with spider jump techniques is used to develop a structural optimization technique for steel space structures. The improved version of SSO uses adaptive randomness probability in generating new solutions. The objective function of the design optimization problem is taken as the weight of a steel space structure. Constraints' functions are implemented from American Institute of Steel Construction-Load Resistance factor design (AISC-LRFD) and Ad Hoc Committee report and practice which cover strength, serviceability and geometric requirements. Three steel space structures are optimized using both standard SSO and SSO with spider jump (SSO_SJ) algorithms and the results are compared with those available in the literature in order to investigate the performance of the proposed algorithms.

• Geomechanics and Engineering
• /
• v.36 no.3
• /
• pp.259-276
• /
• 2024

The undrained shear strength is widely acknowledged as a fundamental mechanical property of soil and is considered a critical engineering parameter. In recent years, researchers have employed various methodologies to evaluate the shear strength of soil under undrained conditions. These methods encompass both numerical analyses and empirical techniques, such as the cone penetration test (CPT), to gain insights into the properties and behavior of soil. However, several of these methods rely on correlation assumptions, which can lead to inconsistent accuracy and precision. The study involved the development of innovative methods using extreme gradient boosting (XGB) to predict the pile set-up component "A" based on two distinct data sets. The first data set includes average modified cone point bearing capacity (q_t), average wall friction (f_s), and effective vertical stress (σ_vo), while the second data set comprises plasticity index (PI), soil undrained shear cohesion (S_u), and the over consolidation ratio (OCR). These data sets were utilized to develop XGBoost-based methods for predicting the pile set-up component "A". To optimize the internal hyperparameters of the XGBoost model, four optimization algorithms were employed: Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), Arithmetic Optimization Algorithm (AOA), and Sine Cosine Optimization Algorithm (SCOA). The results from the first data set indicate that the XGBoost model optimized using the Arithmetic Optimization Algorithm (XGB - AOA) achieved the highest accuracy, with R2 values of 0.9962 for the training part and 0.9807 for the testing part. The performance of the developed models was further evaluated using the RMSE, MAE, and VAF indices. The results revealed that the XGBoost model optimized using XGBoost - AOA outperformed other models in terms of accuracy, with RMSE, MAE, and VAF values of 0.0078, 0.0015, and 99.6189 for the training part and 0.0141, 0.0112, and 98.0394 for the testing part, respectively. These findings suggest that XGBoost - AOA is the most accurate model for predicting the pile set-up component.