Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression

Neural Computing and Applications - The prediction of asphalt performance can be very important in terms of increasing service life and performance while saving energy and money. In this study, a...     

Impact Fatigue Damage of GFRP Materials Due to Repeated Raindrop Collisions

An impact fatigue study has been conducted for GFRP composite laminates to investigate failure mechanisms. A nylon bead with diameter of 4 mm was used as an impactor to simulate raindrop impact. Various specimen thicknesses of 3.0, 4.0 and 5.0 mm were used during experiment. Incident impact velocity of nylon bead ranged between 100 to 220 m/s. Optical microscopic observations were conducted to evaluate the damage at specimen center part of front and back surfaces. SEM investigations were made on the cross-section of damaged specimen. In conclusion, there are three damage modes were found to appear: debonding, matrix cracking, and delamination. Debonding occurred inside specimen at an early stage. Matrix cracking at front speciemens surface was ring crack, and that at back specimen surface was star crack. Delamination was resulted by repeated impacts. Initiation life for each damage mode depends on incident impact energy expressed as an (E–N) diagram of impact fatigue.     

Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams

Engineering with Computers - This study presents a novel artificial intelligence (AI) technique based on two support vector machine (SVM) models and symbiotic organisms search (SOS) algorithm,...     

Fuzzy adaptive teaching–learning-based optimization for global numerical optimization

Teaching–learning-based optimization (TLBO) is one of the latest metaheuristic algorithms being used to solve global optimization problems over continuous search space. Researchers have proposed few variants of TLBO to improve the performance of the basic TLBO algorithm. This paper presents a new variant of TLBO called fuzzy adaptive teaching–learning-based optimization (FATLBO) for numerical global optimization. We propose three new modifications to the basic scheme of TLBO in order to improve its searching capability. These modifications consist, namely of a status monitor, fuzzy adaptive teaching–learning strategies, and a remedial operator. The performance of FATLBO is investigated on four experimental sets comprising complex benchmark functions in various dimensions and compared with well-known optimization methods. Based on the results, we conclude that FATLBO is able to deliver excellence and competitive performance for global optimization.