This research deals with the development and comparison of two data-driven models, i.e., Artificial Neural Network (ANN) and Adaptive Neuro-based Fuzzy Inference System (ANFIS) models for estimation of 28-day compressive strength of concrete for 160 different mix designs. These various mix designs are constructed based on seven different parameters, i.e., 3/4 mm sand, 3/8 mm sand, cement conten...

optimization techniques can be efficiently utilized to achieve an optimal shape for arch dams. this optimal design can consider the conditions of the economy and safety simultaneously. the main aim is to present an applicable and practical model and suggest an algorithm for optimization of concrete arch dams to enhance their seismic performance. to achieve this purpose, a preliminary optimizati...

Modeling and multi-objective optimization of centrifugal pumps is performed at three steps. At the first step, η and NPSHr in a set of centrifugal pump are numerically investigated using commercial software NUMECA. Two metamodels based on the evolved group method of data handling (GMDH) type neural networks are obtained, at the second step, for modeling of η and NPSHr with respect to geometrica...

Virtual machines (VMs) dynamic consolidation is effective to improve the utilization of resources and energy efficiency in cloud environment. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energy performance trade-off, as aggressive consolidation may lead to performance degradation. Current solutions to the problem of host lo...

a sensitive and selective method using combination of two chemometrics methods, principal component analysis (pca) and artificial neural network (ann), and uv-visible spectroscopy has been developed for the determination of indomethacin (idm) in plasma samples . initially the absorbance spectra were processed using pca to noise reduction and data compression. the scores of these pcs were used a...

a novel approach to determine optimal sampling locations under parameter uncertainty in a water distribution system (wds) for the purpose of its hydraulic model calibration is presented. the problem is formulated as a multi-objective optimisation problem under calibration parameter uncertainty. the objectives are to maximise the calibrated model accuracy and to minimise the number of sampling d...

Background: Multiple Sclerosis (MS) is one of the most debilitating disease among young adults. Understanding the disability score (Expanded Disability Status Scale (EDSS)) of these patients is helpful in choosing their treatment process. Calculating EDSS takes a lot of time for Neurologists, so having a way to estimate EDSS can be helpful. This study aimed to estimate the EDSS score of MS pati...

Prediction of building energy consumption plays an important role in conservation, management, and planning. Continuously improving enhancing the performance forecasting models is key to ensuring sustainability systems. In this connection, current paper presented a new improved hybrid model machine learning application for cooling load (CL) heating (HL) residential buildings after studying anal...

Abstract In this research, a radial basis function artificial neural network (RBF-ANN) model was developed to predict the hot deformation flow curves of API X65 pipeline steel. The results of the developed model was compared with the results of a new phenomenological model that has recently been developed based on a power function of Zener-Hollomon parameter and a third order polynomial functio...

Proper performance of water distribution networks (WDNs) plays a vital role in customer satisfaction. The aim this study is to conduct sensitivity analysis evaluate the behavior WDNs analyzed by pressure-driven (PDA) approach and classification technique using an appropriate artificial neural network, namely Group Method Data Handling (GMDH). For purpose, divided into four distinct steps. In fi...