Optimization of thermal characteristics of industrial structures using Analytic Network Process (ANP)

The ever-increasing energy consumption rate in the construction industry has prompted structural engineers and designers to explore innovative ways to reduce energy consumption throughout the construction-to-demolition cycle. To achieve this, improving the thermal and mechanical characteristics of structural and non-structural elements, along with expanding the application of these materials, is paramount. This approach significantly reduces energy consumption and minimizes harmful effects, aligning with the goals of sustainable development. The serviceability characteristics of a structure depend on several parameters that evaluate the thermal behavior of materials. Dynamic heat-transfer analyses of structural components play a critical role in designing energy-efficient new buildings. Thermal conductivity is a key dynamic parameter worth mentioning. However, the thermal conductivity of materials is highly dependent on operating temperatures and moisture content, and little information is available on the performance of insulation materials under actual climatic conditions. Furthermore, temperature profiles in materials are a function of the inside and outside temperatures and thermo-physical properties of the materials. When a heat wave strikes the outer surface of a wall, it travels through the wall and deforms based on the material properties before reaching the inner surface. This phenomenon is referred to as "time lag" and is a critical factor in understanding the thermal behavior of building materials. This study employs an advanced network analysis to determine the most energy-efficient structural panel and steel for constructing the structural components. The selection of the optimal structure is based on key criteria such as thermal conductivity of the panels, time lag, weight of the structure, and cost. By utilizing the Analytic Network Process (ANP) method, the best energy-efficient structure can be chosen. To calculate these parameters, a three-span silo was simulated in the SAP2000V19.2 software

Forecasting Shaharchay River Flow in Lake Urmia Basin using Genetic Programming and M5 Model Tree

Introduction: Precise prediction of river flows is the key factor for proper planning and management of water resources. Thus, obtaining the reliable methods for predicting river flows has great importance in water resource engineering. In the recent years, applications of intelligent methods such as artificial neural networks, fuzzy systems and genetic programming in water science and engineering have been grown extensively. These mentioned methods are able to model nonlinear process of river flows without any need to geometric properties. A huge number of studies have been reported in the field of using intelligent methods in water resource engineering. For example, Noorani and Salehi (23) presented a model for predicting runoff in Lighvan basin using adaptive neuro-fuzzy network and compared the performance of it with neural network and fuzzy inference methods in east Azerbaijan, Iran. Nabizadeh et al. (21) used fuzzy inference system and adaptive neuro-fuzzy inference system in order to predict river flow in Lighvan river. Khalili et al. (13) proposed a BL-ARCH method for prediction of flows in Shaharchay River in Urmia. Khu et al. (16) used genetic programming for runoff prediction in Orgeval catchment in France. Firat and Gungor (11) evaluated the fuzzy-neural model for predicting Mendes river flow in Turkey. The goal of present study is comparing the performance of genetic programming and M5 model trees for prediction of Shaharchay river flow in the basin of Lake Urmia and obtaining a comprehensive insight of their abilities. Materials and Methods: Shaharchay river as a main source of providing drinking water of Urmia city and agricultural needs of surrounding lands and finally one of the main input sources of Lake Urmia is quite important in the region. For obtaining the predetermined goals of present study, average monthly flows of Shaharchay River in Band hydrometric station has been gathered from 1951 to 2011. Then, two third of mentioned data were used for calibration and the rest were used for validation of study models including genetic programming and M5 model trees. It should be noted that for prediction of Shaharchay river flows, previous data of mentioned river in 1, 2 and 3 months ago (Q, Q, Q) were used. Genetic programming: was first proposed by Koza (17). It is a generalization of genetic algorithms. The fundamental difference between genetic programming and genetic algorithm is due to the nature of the individuals. In genetic algorithm, the individuals are linear strings of fixed length (chromosomes). In genetic programming, the individuals are nonlinear entities of different sizes and shapes (parse trees). Genetic programming applies genetic algorithms to a “population” of programs, typically encoded as tree-structures. Trial programs are evaluated against a “fitness function”. Then the best solutions are selected for modification and re-evaluation. This modification-evaluation cycle is repeated until a “correct” program is produced. Model trees generalize the concepts of regression trees, which have constant values at their leaves. So, they are analogous to piece-wise linear functions. M5 model tree is a binary decision tree having linear regression function at the terminal nodes, which can predict continuous numerical attributes. Tree-based models are constructed by a divide-and-conquer method. Results and Discussion: In order to investigate the probability of using different mathematical functions in genetic programming method, three combinations of the functions were used in the current study. The results showed that in the case of predicting river flows with Q, M5 model trees with root mean squared error of 4.7907 in comparison with genetic programming by the best mathematical functions and root mean squared error of 4.8233 had better performances. Obtained results indicated that adding more mathematical functions to the genetic programming and producing more complicated analytical formulations did not have positive effect in reducing prediction error. Unlike the previous observed trend, in case of predicting river flows with Q Q, the genetic programming method with root mean squared error of 3.3501 in comparison with M5 model trees with error of 3.8480 had more satisfied performance. Finally, in the case of predicting river flows with Q, Q,Q, the genetic programming method with root mean squared error of 3.3094 in comparison with M5 model trees with error of 3.5514 presented better predictions. As a result, it can be stated that genetic programming by the best mathematical functions and considering the input parameters of Q,Q,Q, by resulting less root mean squared error and high correlation coefficients had the best performances among others. Also, the results showed that adding more trigonometric functions did not improve the precisions of the predictions. Conclusion: In this research, the intelligent models such as genetic programming and M5 model trees have been used for prediction of monthly flows of Shaharchay River located in East Azerbaijan, Iran. The obtained results showed that the genetic programming by the best mathematical functions and M5 model trees in case of considering the input parameters of Q,Q,Q, by less root mean squared error had the best performances in river flow predictions. As a conclusion, the genetic programming method by specific mathematical functions including four basic operations, logarithm, power and using input parameters of Q,Q,Q, has been proposed as the best and precise model for predicting Shaharchay River flows

Estimación mediante programación genética de los patrones del suelo humectantes para el riego por goteo

Drip irrigation is considered as one of the most efficient irrigation systems. Knowledge of the soil wetted perimeter arising from infiltration of water from drippers is important in the design and management of efficient irrigation systems. To this aim, numerical models can represent a powerful tool to analyze the evolution of the wetting pattern during irrigation, in order to explore drip irrigation management strategies, to set up the duration of irrigation, and finally to optimize water use efficiency. This paper examines the potential of genetic programming (GP) in simulating wetting patterns of drip irrigation. First by considering 12 different soil textures of USDA–SCS soil texture triangle, different emitter discharge and duration of irrigation, soil wetting patterns have been simulated by using HYDRUS 2D software. Then using the calculated values of depth and radius of wetting pattern as target outputs, two different GP models have been considered. Finally, the capability of GP for simulating wetting patterns was analyzed using some values of data set that were not used in training. Results showed that the GP method had good agreement with results of HYDRUS 2D software in the case of considering full set of operators with R2 of 0.99 and 0.99 and root mean squared error of 2.88 and 4.94 in estimation of radius and depth of wetting patterns, respectively. Also, field experimental results in a sandy loam soil with emitter discharge of 4 L h-1 showed reasonable agreement with GP results. As a conclusion, the results of the study demonstrate the usefulness of the GP method for estimating wetting patterns of drip irrigation.El riego por goteo está considerado como uno de los sistemas de riego más eficientes. El conocimiento del perímetro del bulbo mojado durante la fase de infiltración del agua es importante para el proyecto y manejo de sistemas de riego por goteo eficientes. Los modelos numéricos son una herramienta útil para analizar la evolución del bulbo mojado durante el riego a fin de explorar estrategias de manejo del riego por goteo que determinen el tiempo de riego y optimicen la eficiencia del uso del agua. En este trabajo se examinó el potencial de la programación de algoritmos genéticos (GP) para la simulación de la forma de bulbos mojados en riego por goteo. En primer lugar se ha simulado, con el programa de métodos numéricos HYDRUS 2D, el bulbo mojado en 12 texturas de suelo y diferentes caudales de goteros y tiempos de riego. A partir de las estimaciones de la profundidad y radio mojado como variables objetivo, se han considerado dos modelos GP diferentes. Por último, se ha analizado la capacidad de GP para simular la forma del bulbo mojado a partir de valores que no se utilizaron durante el proceso de entrenamiento. Los resultados obtenidos con GP, considerando el conjunto completo de operadores, se ajustaron, razonablemente, a los estimados con HYDRUS 2D, obteniéndose en la estimación del radio y la profundidad del bulbo mojado, coeficientes R2= 0,99 en ambos casos y valores de error cuadrático medio de 2,88 y 4,94 respectivamente. Los resultados experimentales de campo en un suelo franco arenoso con caudal del emisor de 4 L h-1 concordaron razonablemente con los de GP. Los resultados del estudio demuestran la utilidad de este método para estimar la forma del bulbo mojado en riego por goteo

Comparison of machine learning techniques for predicting porosity of chalk

202202 bcvcNot applicableSelf-fundedPublished24 month

Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data

Long-term windspeed prediction is crucial for establishing the viability of wind as a clean energy option, including the selection of wind farm locations, feasibility studies on energy potential and the operation of wind energy conversion systems with minimal investment risk. To deliver this vital societal need, data-inexpensive artificial intelligence models relying on historical inputs can be a useful scientific contrivance by energy analysts, engineers and climate-policy advocates. In this paper, a novel approach is adopted to construct a multilayer perceptron (MLP) hybrid model integrated with the Firefly Optimizer algorithm (MLP-FFA) trained with a limited set of historical (monthly) data (2004–2014) for a group of neighboring stations to predict windspeed at target sites in north-west Iran. Subsequently, the MLP-FFA model is developed to minimize the error rate of the resulting hybrid model and applied at each of the eight target sites one-by-one (namely: Tabriz, Jolfa, Sarab, Marand, Sahand, Kaleybar, Maraghe and Mianeh) such that the seven neighboring (reference) sites are used for training and the remainder eighth site for testing purposes. To ascertain conclusive results, the hybrid model's ability to predict windspeed at each target site is cross-validated with the MLP model without the FFA optimizer and the statistical performance is benchmarked with root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (E NS ), Willmott's Index (d) and the Legates and McCabes Index (E 1 ), including relative errors. For all eight target sites, the testing performance of the MLP-FFA model is found to be significantly superior than the classical MLP, resulting in lower values of the RMSE (0.202–0.50 ms -1 relative to 0.236–0.664 ms -1 ) and larger values of E NS , d and E 1 (0.686–0.953 vs. 0.529–0.936, 0.874–0.976 vs. 0.783–0.966, 0.417–0.800 vs. 0.303–0.748). Despite a more accurate performance of hybrid models tested at each target site, the preciseness registered a distinct geographic signature with the least accurate result (for Kaleybar) and the most accurate result (for Jolfa). To accord with this result, we conclude that the utilization of the FFA as an add-in optimizer in a hybrid data-intelligent model leads to a significant improvement in the predictive accuracy, presumably due to the optimal weights attained in the hidden layer that allows a more robust feature extraction process. Accordingly, we establish that the hybrid MLP-FFA model can be explored further in a problem of long-term windspeed prediction with reference station input data, and feasibility studies on wind energy investments in data-scarce regions where a limited set of neighboring reference site data can be employed to forecast the target site windspeed. © 2017 Elsevier LtdChinese Academy of Agricultural Sciences: ADOSP 2016The data were acquired from Iranian Meteorological Organization which is greatly acknowledged. Dr R C Deo was supported by a grant through Chinese Academy of Science Presidential Fellowship and Academic Development and Outside Studies Program (ADOSP 2016) in writing phase, and short-term ADOSP funding (s-ADOSP 2017) in the revision phase. We acknowledge all three reviewers and Editor-in-Chief Prof. Soteris Kalogirou for their critical comments that have improved the clarity of our final paper

Estimating soil wetting patterns for drip irrigation using genetic programming

Drip irrigation is considered as one of the most efficient irrigation systems. Knowledge of the soil wetted perimeter arising from infiltration of water from drippers is important in the design and management of efficient irrigation systems. To this aim, numerical models can represent a powerful tool to analyze the evolution of the wetting pattern during irrigation, in order to explore drip irrigation management strategies, to set up the duration of irrigation, and finally to optimize water use efficiency. This paper examines the potential of genetic programming (GP) in simulating wetting patterns of drip irrigation. First by considering 12 different soil textures of USDA�SCS soil texture triangle, different emitter discharge and duration of irrigation, soil wetting patterns have been simulated by using HYDRUS 2D software. Then using the calculated values of depth and radius of wetting pattern as target outputs, two different GP models have been considered. Finally, the capability of GP for simulating wetting patterns was analyzed using some values of data set that were not used in training. Results showed that the GP method had good agreement with results of HYDRUS 2D software in the case of considering full set of operators with R2 of 0.99 and 0.99 and root mean squared error of 2.88 and 4.94 in estimation of radius and depth of wetting patterns, respectively. Also, field experimental results in a sandy loam soil with emitter discharge of 4 L h-1 showed reasonable agreement with GP results. As a conclusion, the results of the study demonstrate the usefulness of the GP method for estimating wetting patterns of drip irrigation

Estimating soil wetting patterns for drip irrigation using genetic programming

Drip irrigation is considered as one of the most efficient irrigation systems. Knowledge of the soil wetted perimeter arising from infiltration of water from drippers is important in the design and management of efficient irrigation systems. To this aim, numerical models can represent a powerful tool to analyze the evolution of the wetting pattern during irrigation, in order to explore drip irrigation management strategies, to set up the duration of irrigation, and finally to optimize water use efficiency. This paper examines the potential of genetic programming (GP) in simulating wetting patterns of drip irrigation. First by considering 12 different soil textures of USDA-SCS soil texture triangle, different emitter discharge and duration of irrigation, soil wetting patterns have been simulated by using HYDRUS 2D software. Then using the calculated values of depth and radius of wetting pattern as target outputs, two different GP models have been considered. Finally, the capability of GP for simulating wetting patterns was analyzed using some values of data set that were not used in training. Results showed that the GP method had good agreement with results of HYDRUS 2D software in the case of considering full set of operators with R2 of 0.99 and 0.99 and root mean squared error of 2.88 and 4.94 in estimation of radius and depth of wetting patterns, respectively. Also, field experimental results in a sandy loam soil with emitter discharge of 4 L h–1 showed reasonable agreement with GP results. As a conclusion, the results of the study demonstrate the usefulness of the GP method for estimating wetting patterns of drip irrigation

Forecasting the discharge capacity of inflatable rubber dams using hybrid machine learning models

202303 bcwwVersion of RecordOthersKJGG004, KJGG219; Technische Universität Dresden, TUD; Natural Science Foundation of Henan Province: 182300410291Publishe