Metaheuristic optimization of reinforced concrete footings

The primary goal of an engineer is to find the best possible economical design and this goal can be achieved by considering multiple trials. A methodology with fast computing ability must be proposed for the optimum design. Optimum design of Reinforced Concrete (RC) structural members is the one of the complex engineering problems since two different materials which have extremely different prices and behaviors in tension are involved. Structural state limits are considered in the optimum design and differently from the superstructure members, RC footings contain geotechnical limit states. This study proposes a metaheuristic based methodology for the cost optimization of RC footings by employing several classical and newly developed algorithms which are powerful to deal with non-linear optimization problems. The methodology covers the optimization of dimensions of the footing, the orientation of the supported columns and applicable reinforcement design. The employed relatively new metaheuristic algorithms are Harmony Search (HS), Teaching-Learning Based Optimization algorithm (TLBO) and Flower Pollination Algorithm (FPA) are competitive for the optimum design of RC footings

Optimum design of reinforced concrete retaining walls with the flower pollination algorithm

The flower pollination algorithm (FPA) is anefficient metaheuristicoptimizationalgorithm mimickingthe pollinationprocessof flowering species. In this study, FPA is applied, for first time, to the optimum design of reinforced concrete (RC) cantilever retaining walls. It is foundthat FPA offers important savings with respect to conventional design approachesand that it outperformsgenetic algorithm (GA)andthe particle swarm optimization (PSO) algorithm in this designproblem.Furthermore, parameter tuning reveals that the best FPA performance is achieved for switch probability values ranging between 0.4 and 0.7, a population size of 20 individualsand aLévy flightstep sizescale factor of 0.5. Finally, parametric optimum designs show that theoptimumcost of RC retaining walls increases rapidly with the wallheight and smoothly with the magnitude of surcharge loadin

Social Algorithms

This article concerns the review of a special class of swarm intelligence based algorithms for solving optimization problems and these algorithms can be referred to as social algorithms. Social algorithms use multiple agents and the social interactions to design rules for algorithms so as to mimic certain successful characteristics of the social/biological systems such as ants, bees, bats, birds and animals.Comment: Encyclopedia of Complexity and Systems Science, 201

Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering

In today’s developing world, industries are constantly required to improve and advance. New approaches are being implemented to determine optimum values and solutions for models such as artificial intelligence and machine learning. Research is a necessity for determining how these recent methods are being applied within the engineering field and what effective solutions they are providing.Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering is a collection of innovative research on the methods and implementation of machine learning and AI in multiple facets of engineering. While highlighting topics including control devices, geotechnology, and artificial neural networks, this book is ideally designed for engineers, academicians, researchers, practitioners, and students seeking current research on solving engineering problems using smart technology.Используемые программы Adobe AcrobatВ современном развивающемся мире отрасли промышленности постоянно нуждаются в совершенствовании и продвижении вперед. Внедряются новые подходы для определения оптимальных значений и решений для таких моделей, как искусственный интеллект и машинное обучение. Исследования необходимы для определения того, как эти новейшие методы применяются в области инженерии и какие эффективные решения они предоставляют. Приложения искусственного интеллекта и машинного обучения в гражданском, механическом и промышленном проектировании - это сборник инновационных исследований по методам и внедрению машинного обучения и искусственного интеллекта в различных областях инженерии. Освещая такие темы, как устройства управления, геотехнологии и искусственные нейронные сети, эта книга идеально предназначена для инженеров, академиков, исследователей, практиков и студентов, ищущих актуальные исследования по решению инженерных задач с использованием интеллектуальных технологий

COST AND CO2 EMISSION-BASED OPTIMISATION OF REINFORCED CONCRETE DEEP BEAMS USING JAYA ALGORITHM

The environmental impact of producing construction materials is being increasingly analysed in the recent years. This study aims at demonstrating the possibility of reducing the carbon emissions and cost of a structure while at the same keeping the structural performance at a satisfactory level. Optimisation algorithms play a crucial role in this process. In this study a metaheuristic algorithm called the Jaya algorithm was implemented in order to minimise the carbon emission and the cost associated with the production process of concrete and steel. As the indicator of structural performance, the ultimate shear strength was used. Optimum values of reinforcement ratio, concrete compressive strength and cross-sectional dimensions are obtained at different levels of the ultimate shear strength. The results obtained through the CO2 and cost optimisations are compared. It was demonstrated that for a given value of expected ultimate shear strength the optimum design can make nearly an order of magnitude difference in the cost and carbon emission

Space Applications of Tensegric Structures

Tensile integrity (tensegrity, tensegric) structures are suggested for possible use in outer space and on space bodies because of their minimal need for structural materials, ease of transfer and mounting. Structurally their behavior is non-linear. It has been shown here that their structural analysis can successfully be performed by total potential optimization using meta-heuristic algorithms (TPO/MA). The named method is an emerging technique that combines minimum energy principle of mechanics with metaheuristic algorithms. The technique thus defined has been shown to be very efficient, accurate and robust in analyzing structures. The success of the technique is especially profound for non-linear structures. In this study the named technique is applied to tensile integrity (tensegrity, tensegric) structures which are known to be highly nonlinear, thus difficult to analyze