In this paper the artificial neural network used to predict dilly evaporation. The model was trained in MATLAB with five inputs. The inputs are Min. Temperature, Max. Temperature, average temperature, wind speed and humidity. The data collected from Alramadi meteorological station for one year. The transfer function models are sigmoid and tangent sigmoid in hidden and output layer, it is the most commonly used nonlinear activation function. The best numbers of neurons used in this paper was three nodes. The results concludes, that the artificial neural network is a good technique for predicting daily evaporation, the empirical equation can be used to compute daily evaporation (Eq.6) with regression more than 96% for all (training, validation and testing) as well as, in this model that the Max. Temperature is a most influence factor in evaporation with importance ratio equal to (30%) then humidity (26%).
Pile foundations are typically employed when top-soil layers are unstable and incapable of bearing super-structural pressures. Accurately modeling pile behavior is crucial for ensuring optimal structural and serviceability performance. However, traditional methods such as pregnancy testing, while highly accurate, are expensive and time-consuming. Consequently, various approaches have been developed to predict load settlement behavior, including using artificial neural networks (ANNs). ANNs offer the advantage of accurately replicating substrate behavior's nonlinear and intricate relationship without requiring prior formulation.This research aims to employ artificial neural network (ANN) modeling techniques to simulate the load-settlement relationship of drilled piles. The primary aims of this study are threefold: firstly, to assess the effectiveness of the generated ANN model by comparing its results with experimental pile load test data; secondly, to establish a validation method for ANN models; and thirdly, to conduct a sensitivity analysis to identify the significant input factors that influence the model outputs. In addition, this study undertakes a comprehensive review of prior research on using artificial neural networks for predicting pile behavior. Evaluating efficiency measurement indicators demonstrates exceptional performance, particularly concerning the agreement between the predicted and measured pile settlement. The correlation coefficient (R) and coefficient of determination (R^2) indicate a strong correlation between the predicted and measured values, with values of 0.965 and 0.938, respectively. The root mean squared error (RMSE) is 0.051, indicating a small deviation between the predicted and actual values. The mean percentage error (MPE) is 11%, and the mean absolute percentage error (MAPE) is 21.83%.
In the current article, an experimental investigation has been implemented of flow and heat transfer characteristics in a parabolic trough solar collector (PTSC) using both nano-fluids and artificial neural networks modeling. Water was used as a standard working fluid in order to compare with two different types of nano-fluid namely, nano-CuO /H2O and nano-TiO2/ H2O, both with a volume concentration of 0.02. The performance of the PTSC system was eval-uated using three main indicators: outlet water temperature, useful energy and thermal efficiency under the influence of mass flowrate ranging from 30 to 80 Lt/hr. In parallel, an artificial neural network (ANN) has been proposed to predict the thermal efficiency of PTSC depending on the experimental re-sults. An Artificial Neural Network (ANN) model consists of four inputs, one output parameter and two hidden layers, two neural network models (4-2-2-1) and (4-9-9-1) were built. The experimental results show that CuO/ H2O and TiO2/H2O have higher thermal performance than water. Overall, it was veri-fied that the maximum increase in thermal efficiency of TiO2/H2O and CuO/H2O compared to water was 7.12% and 19.2%, respectively. On the oth-er hand, the results of the model 4-9-9-1 of ANN provide a higher reliability and accuracy for predicting the Thermal efficiency than the model 4-2-2-1. The results revealed that the agreement in the thermal efficiency between the ANN analysis and the experimental results about of 91% and RMSE 3.951 for 4-9-9-1 and 86% and RMSE 5.278 for 4-2-21.
One way of obtaining information about reliability of units is to accelerate their life by testing at higher levels of stress (such as increasing elevated temperatures or voltages). Predicting the lifetime of a unit at normal operating conditions based on data collected at accelerated conditions is a common objective of these tests. Different models of accelerated life testing are used for such extrapolations. Two statistical based models are widely used: parametric models which require a prior specified lifetime distribution, and nonparametric models that relax of the assumption of the life time distribution. The proportional odds model is a nonparametric model in accelerated life testing based on the odds function and show that it gives a more accurate reliability estimates than proportional hazard model. This paper will concentrate on the models of proportional odds nonparametric accelerated life test for reliability prediction.