Environmental impacts cost assessment model of residential building using an artificial neural network

Purpose: Buildings are major contributors to greenhouse gases (GHG) along the various stages of the building life cycle. A range of tools have been utilised for estimating building energy use and environmental impacts; these are time-consuming and require massive data that are not necessarily available during early design stages. Therefore,...

Construction of three-dimensional extrusion limit diagram for magnesium alloy using artificial neural network and its validation

Conventional extrusion limit diagram (ELD) involves only two extrusion process variables and as such it does not account for the combined effects of multiple process parameters on the extrusion process with respect to pressure requirement and extrudate temperature. Attempts were made in the present research to construct three-dimensional (3D)...

Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting

Participation of wind energy in the generation portfolio of power systems is increasing, making it more challenging for system operators to adequately maintain system security. It therefore becomes increasingly crucial to accurately predict the wind generation. This work investigates how different parameters influence the performance of...

Solving vapor-liquid flash problems using artificial neural networks

Vapor-liquid phase equilibrium —flash— calculations largely contribute to the total computation time of many process simulations. As a result, process simulations, especially dynamic ones, are limited in the amount of detail that can be included due to simulation time restrictions. In this work, artificial neural networks were investigated...

A neural network-based framework for financial model calibration

A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal values of the model parameters is formulated as training hidden neurons within a machine learning framework, based on available financial option prices. The...

ANN-Based Shear Capacity of Steel Fiber-Reinforced Concrete Beams without Stirrups

Comparing experimental results of the shear capacity of steel fiber-reinforced concrete (SFRC) beams without stirrups to the capacity predicted using current design equations and other available formulations shows that predicting the shear capacity of SFRC beams without mild steel shear reinforcement is still difficult. The reason for this...

ANN-Based Fatigue Strength of Concrete under Compression

When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we study the reduced capacity as a function of a given number of cycles by means of artificial neural networks. We used...

A Methodology for daylight optimisation of high-rise buildings in the dense urban district using overhang length and glazing type variables with surrogate modelling

Urbanization and population growth lead to the construction of higher buildings in the 21st century. This causes an increment on energy consumption as the amount of constructed floor areas is rising steadily. Integrating daylight performance in building design supports reducing the energy consumption and satisfying occupants’ comfort. This study...

Active vision via extremum seeking for robots in unstructured environments: Applications in object recognition and manipulation

In this paper, a novel active vision strategy is proposed for optimizing the viewpoint of a robot's vision sensor for a given success criterion. The strategy is based on extremum seeking control (ESC), which introduces two main advantages: 1) Our approach is model free: It does not require an explicit objective function or any other task...