Application of a novel metaheuristic algorithm inspired by stadium spectators in global optimization problems.

This paper presents a novel metaheuristic algorithm inspired by the actions of stadium spectators affecting behavior of players during a match which will be called stadium spectators optimizer (SSO) algorithm. The mathematical model of the SSO algorithm is presented and the performance and efficiency of the presented method is tested on some of the well-known mathematical test functions and also CEC-BC-2017 functions. The SSO algorithm is a parameter-free optimization method since it doesn't require any additional parameter setup at any point throughout the optimization process. It seems urgently necessary to design a novel metaheuristic algorithm that is parameter-free and capable of solving any optimization problem without taking into account extra parameters, as the majority of metaheuristic algorithms rely on the configuration of extra parameters to solve different problems efficiently. A positive point for the SSO algorithm can be seen in the results of the suggested technique, which indicate a partial improvement in performance. The results are compared with those of golf optimization algorithm (GOA), Tiki taka optimization algorithm (TTA), Harris Hawks optimization algorithm (HHO), the arithmetic optimization algorithm (AOA), CMA-ES and EBOwithCMAR algorithms. The statistical tests are carried out for the obtained results and the tests reveal the capability of the presented method in solving different optimization problems with different dimensions. SSO algorithm performs comparably and robustly with the state-of-the-art optimization techniques in 14 of the mathematical test functions. For CEC-BC-2017 functions with ten dimensions, EBOwithCMAR performs better than the proposed method. However, for most functions of CEC-BC-2017 with ten dimensions, the SSO algorithm ranks second after EBOwithCMAR, which is an advantage of the SSO since the proposed method performs better than the well-known CMA-ES optimization algorithm. The overall performance of the SSO algorithm in CEC-BC-2017 functions with 10 dimensions was acceptable, in dimension of 30, 50 and 100, the performance of the proposed method in some functions decreased.

Response Attenuation of a Structure Equipped with ATMD under Seismic Excitations Using Methods of Online Simple Adaptive Controller and Online Adaptive Type-2 Neural-Fuzzy Controller.

The present study aims to design a robust adaptive controller employed in the active tuned mass damper (ATMD) system to overcome undesirable vibrations in multistory buildings under seismic excitations. We propose a novel adaptive type-2 neural-fuzzy controller (AT2NF). All system parameters are taken as unknowns. The MLP neural network is used to extract the Jacobian and estimate the structural model; then, the estimated model is applied to the controller online. To tune the control force applied to the ATMD and achieve the control targets, the controller parameters are adaptively trained using the extended Kalman Filter (EKF) and the error back-propagation algorithm. A PID controller is also included in this method to increase the stability and robustness of the adaptive type-2 neural-fuzzy controller against seismic vibrations. An online simple adaptive controller (OSAC) is studied to demonstrate the suggested controller's superiority. The OSAC is based on adaptive control of the implicit reference model. In this proposed method, the EKF is used to tune the controller parameters online as a novel feature. The uncertainty associated with identifying the mechanical properties of structures, such as mass and stiffness, is one of the primary challenges in the real-time control of structures. This paper investigates how both controllers cope with parametric uncertainties under far-field and near-field seismic excitation. According to numerical results, the AT2NF controller outperforms OSAC in minimizing the dynamic responses of the structure during an earthquake and accomplishing control objectives when the structure's characteristics change.

Application of Polyacrylic Hydrogel in Durability and Reduction of Environmental Impacts of Concrete through ANN.

While adding superabsorbent polymer hydrogel particles to fresh concrete admixtures, they act as internal curing agents that absorb and then release large amounts of water and reduce self-desiccation and volumetric shrinkage of cement that finally result in hardened concrete with increased durability and strength. The entrainment of microscopic air bubbles in the concrete paste can substantially improve the resistance of concrete. When the volume and distribution of entrained air are adequately managed, the microstructure is protected from the pressure produced by freezing water. This study addresses the design and application of hydrogel nanoparticles as internal curing agents in concrete, as well as new findings on crucial hydrogel-ion interactions. When mixed into concrete, hydrogel particles produce their stored water to power the curing reaction, resulting in less volumetric shrinkage and cracking and thereby prolonging the service life of concrete. The mechanical and swelling performance qualities of the hydrogel are very sensitive to multivalent cations found naturally in concrete mixes, such as aluminum and calcium. The interactions between hydrogel nanoparticles and alkaline cementitious mixes are described in this study, while emphasizing how the chemical structure and shape of the hydrogel particles regulate swelling behavior and internal curing efficiency to eliminate voids in the admixture. Moreover, in this study, an artificial neural network (ANN) was utilized to precisely and quickly analyze the test results of the compressive strength and durability of concrete. The addition of multivalent cations reduced swelling capacity and changed swelling kinetics, resulting in fast deswelling behavior and the creation of a mechanically stiff shell in certain hydrogel compositions. Notably, when hydrogel particles were added to a mixture, they reduced shrinkage while encouraged the creation of particular inorganic phases within the void area formerly held by the swelled particle.

Mechanical Characteristics and Self-Healing Soil-Cementitious Hydrogel Materials in Mine Backfill Using Hybridized ANFIS-SVM.

The compressive strength, shrinkage, elasticity, and electrical resistivity of the cement-soil pastes (slag, fly ash) of self-healing of cementitious concrete have been studied while adding hydrogels with nano silica (NSi) in this research. Defining the hydraulic and mechanical properties of these materials requires improvement to motivate more uptake for new buildings. Initially, examining the impact of different synthesized hydrogels on cement-soil pastes showed that solid particles in the mixtures highly affected the absorption capacity of NSi, representing the importance of direct interactions between solid particles and hydrogels in a cementitious matrix. All test results were analyzed by use of a hybridized soft computing model such as the adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR) for precise studying and the avoidance of few empirical tests or error percentages. Subsequently, the best RMSE of ANFIS is 0.6568 and the best RMSE of SVM is 1.2564; the RMSE of ANFIS-SVM (0.5643) in the test phase is also close to zero, showing a better performance in hypothesizing self-healing soil-cementitious hydrogel materials in mine backfill. The R2 value for ANFIS-SVM is 0.9547, proving that it is a proper model for predicting the study's goal. Electrical resistivity and compressive strength declined in the cement-soil pastes including hydrogels according to experimental outcomes; it was lowered by the increase of NSi concentration in the hydrogel. There was a decrement in the autogenous shrinkage of cement-soil pastes while adding hydrogel, depending on the NSi concentration in the hydrogels. The findings of this research are pivotal for the internal curing of cementitious materials to define the absorption of hydrogels.

The Numerical Analysis of Replenishment of Hydrogel Void Space Concrete Using Hydrogels Containing Nano-Silica Particles through ELM-ANFIS.

Currently, Nano-materials are gaining popularity in the building industry due to their high performance in terms of sustainability and smart functionality. In order to reduce cement production and CO2 emissions, nano-silica (NS) has been frequently utilized as a cement alternative and concrete addition. The influence of Nano-silica-containing hydrogels on the mechanical strength, electrical resistivity, and autogenous shrinkage of cement pastes was investigated. The goal of this study was to identify the main structure-property relationships of water-swollen polymer hydrogel particles used as internal curing agents in cementitious admixtures, as well as to report a unique synthesis process to combine pozzolanic materials with hydrogel particles and determine the replenishment of hydrogel void space. Experiments were designed to measure the absorption capacity and kinetics of hydrogel particles immersed in pure water and cementitious pore solution, as well as to precisely analyze the data derived from the tests using hybridized soft computing models such as Extreme learning machine (ELM) and Adaptive neuro-fuzzy inference system (ANFIS). The models were developed, and the findings were measured using regression indices (RMSE and R2). The findings indicated that combining nano-silica with polymeric hydrogel particles creates a favorable environment for the pozzolanic reaction to occur, and that nano-silica assists in the refilling of hydrogel void space with hydrated cement phases.