دین محمد ایمانی

The increasing progress of technology has made building information modeling as one of the new technologies in the growing construction industry and has attracted the attention of researchers for its use and how to develop it.The aim of the current research was to identify and rank the challenges of implementing building information modeling using the fuzzy BWM approach. The research method is based on the quantitative nature, according to the practical purpose, and based on the method of data collection, which is a survey type. In addition, the new best-worst method has been used to solve the multi-criteria decision making problem in the fuzzy space. The statistical population is 10 experts in the field of construction and equal to the statistical sample .The research tool was a researcher-made questionnaire and a standard BWM questionnaire. It was used to check content validity and Cronbach's alpha coefficient was used to check reliability. The findings of the research found the existence of 15 indicators in three social, tactical and economic-management sectors as effective challenges of building modeling implementation with the BWM method. In addition, the tactical criterion with a weight of 499/499 has won the first rank, the social criterion with a weight of 266/2 has won the second rank, and the economic-management criterion with a weight of 234/2 has won the third rank with this approach. Also, the weighting and ranking of each sub-criterion is specified, suggestions and solutions to solve the existing challenges have been presented.

Sugarcane is a strategic agricultural product and increasing productivity and self-sufficiency in its production is of special importance. The most important product of sugarcane is sugar. Various factors like climatic and management conditions affect the yield of sugarcane and recoverable sugar. Crop yield forecasting is one of the most important topics in precision agriculture, which is used to estimate yield, match product supply with demand and manage product to increase productivity. The purpose of this study is to predict and model the factors affecting sugar extracted from sugarcane (recoverable sugar) in the farms of Amir-Kabir sugarcane agro-industry Company of Khuzestan province using machine learning methods.

To conduct this study, data from the agro-industrial company Amir-Kabir in the province of Khuzestan from 2010 to 2017 were used. This data has 3223 records which include four sets of data: climate, soil, crop and farm management. This data includes continuous and discrete variables. Discrete variables include production management, soil type, farm, variety, age (cane class), the month of harvest and times irrigation. Continuous variables include area, chemical fertilizer consumption, water consumption per hectare, total water consumption, drain, crop season duration, yield (cane yield) soil EC, purity, time interval drying off to crop harvest, precipitation, min and max temperature, min and max relative humidity, wind speed and evaporation. The recoverable sugar variable is considered as the target variable and is divided into two classes, values greater than or equal to 9 are in the optimal class and less than 9 are in the undesirable class. The other variables are considered as predictor variables. For modeling using the Holdout method the data were randomly divided into two independent sets, a training set and a test set. 70% of the data which includes 2256 records were used for training and 30% of the data which includes 967 records were used for testing. The modeling of this study was performed with the Python programming language version 3.8.6 in the Jupyter notebook environment. Random Forest, Adaboost, XGBoost and SVM (support vector machine) algorithms were used for modeling.

To evaluate the models, metrics of accuracy, precision, recall, f1 score and k-fold cross validation were used. The XGBoost model with 94.8% accuracy on the training set and the Adaboost model with 92.4% accuracy on the test set, are the best models. Based on precision and recall metrics Adaboost model with 87% precision and SVM model with 87% recall have better performance than the other models. Based on Repeated 10-fold stratified cross validation using two repeats the SVM model with 92.3% accuracy is the best model. The variables of purity, time interval drying off to crop harvest and crop season duration are the most important variables in predicting the recoverable sugar.

In this study a new approach based on machine learning methods for predicting recoverable sugar from sugarcane was presented. The most important innovation of this study is the simultaneous consideration of management and climatic factors, along with other factors such as soil and crop characteristics for modeling and classification the recoverable sugar percentage from sugarcane. The results show that the performance of all models is acceptable and machine learning methods and ensemble learning algorithms can be used to predict crop yield. The results of this study and the analysis of the rules obtained from the set of decision trees made in the random forest model can be used for managers of different agro-industries in determining appropriate strategies and preparing the conditions to achieve optimal production.For future research as well as policy making and decision making Amir-Kabir sugarcane agro-industry Company the following suggestions are offered: more samples can be used to obtain more reliable results. Also can be used Deep learning methods, time series analysis and image processing. Use of IOT equipment to collect and real-time processing data on Amir-Kabir sugarcane agro-industry farms.

Manufacturing polymer concrete (PC) with suitable performance against cracking is an important issue for using these composite materials in practical applications. Fracture toughness and fracture energy of such randomly distributed aggregates inside the matrix of polymeric resin is affected by the mix design and percentages of PC ingredients. In this research, the optimum composition of epoxy base PC mixture made of four ingredients (epoxy resin, fine and coarse silica aggregate) was obtained using a L8 Taguchi design of experiment method. Some fracture toughness tests under mode I were conducted on Single edge notched bending specimen according to design of experiment suggested by Taguchi method. The initial range for each PC ingredient was selected as: 25-31% fine aggregate, 21-23% epoxy resin, and 46-54% coarse aggregate, and it was found that the mixture containing maximum resin (23%), maximum fine filler, minimum percentages of fiber and coarse aggregate can provide the highest fracture energy and fracture toughness values.

The use of resin based cement is a common method for strengthening and retaliation the damaged bones material. However due to lack of sufficient adhesion in the interface of two materials (i.e. bone and injected cement) a crack can be initiated in the interface and consequently it can be fractured due to application of external loads to the repaired bone part. In order to investigate the load carrying capacity and reliability of bi-material joint of bone's soft tissue-hydroxyl apatite cement, a number of bi-material bone-cement specimens in the shape of circular disc containing a center crack in the interface of disc and subjected to diametral compression were tested. The bi-material specimens were load under different inclination angles of crack related to the loading direction. This results in application of different mixed mode I+II (i.e. tensile-shear deformation) in the interface of center crack. The results showed that the fracture load and fracture energy becomes more by increasing the crack inclination angle (i.e. increasing the contribution of shear mode deformation relative to mode I component). In addition the overall strength of bi-material bone-cement system was higher than the neat one material bone material. The fracture of all tested bi-material samples was extended along the interface line of Brazilian disc specimen with no kinking into any of two bone or cement materials.