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Analyzing Customers' Demands for Different Housing Features in Buildings Using a Data Mining Method
(Mdpi, 2023) Keles, Abdullah Emre; Arikan, Yusuf Can
There are many options and factors in the production phase of housing. In the marketing phase, houses are presented to the customer's taste. Therefore, it is clear that a customer-oriented approach is necessary to establish a supply-demand balance in housing production on the basis of quality. This study aimed to determine customers' housing demands in the construction sector. Within the scope of the study, 303 surveys were conducted in 30 different provinces of Turkey. The data obtained were analyzed by WEKA software with association rule extraction as the data mining method. The distribution of other attributes was determined according to two different class labels, namely the ownership status of the houses (tenant or homeowner) and customers' expectations of the houses. As a result of the study, it is clear that people living in Turkey prefer a south-facing facade when purchasing a house. In addition, it is seen that the property owners demand 4 + 1 independent units. It is remarkable that individuals who are tenants want the living room to be spacious. The results of the study also revealed that female individuals have higher expectations of housing than male individuals. At the same time, it is understood that people's expectations of housing differ according to the variables of age, education level, and the number of family members. The majority of the results in this study had a confidence value of 90% and above. This study was intended to serve as a guide for housing developers in Turkey to better understand and meet the demands of buildings' residents.
Analyzing Customers' Demands for Different Housing Features in Buildings Using a Data Mining Method
(Mdpi, 2023) Keles, Abdullah Emre; Arikan, Yusuf Can
There are many options and factors in the production phase of housing. In the marketing phase, houses are presented to the customer's taste. Therefore, it is clear that a customer-oriented approach is necessary to establish a supply-demand balance in housing production on the basis of quality. This study aimed to determine customers' housing demands in the construction sector. Within the scope of the study, 303 surveys were conducted in 30 different provinces of Turkey. The data obtained were analyzed by WEKA software with association rule extraction as the data mining method. The distribution of other attributes was determined according to two different class labels, namely the ownership status of the houses (tenant or homeowner) and customers' expectations of the houses. As a result of the study, it is clear that people living in Turkey prefer a south-facing facade when purchasing a house. In addition, it is seen that the property owners demand 4 + 1 independent units. It is remarkable that individuals who are tenants want the living room to be spacious. The results of the study also revealed that female individuals have higher expectations of housing than male individuals. At the same time, it is understood that people's expectations of housing differ according to the variables of age, education level, and the number of family members. The majority of the results in this study had a confidence value of 90% and above. This study was intended to serve as a guide for housing developers in Turkey to better understand and meet the demands of buildings' residents.
Construction Crew Productivity Prediction By Using Data Mining Methods
(Elsevier Science Bv, 2014) Kaya, Mumine; Keles, Abdullah Emre; Oral, Emel Laptali
Ceramic tiling industry has become one of Turkey's fastest growing industries due to the outstanding achievements of Turkish ceramic producers with respect to producing high quality products with lower costs than their equivalents worldwide. Conversely high costs of the end product of Turkish building industry in general show that there is an important problem with the productivity and quality of construction crews. That's why most construction firms begin to realize the need for a detailed research on the factors affecting construction crew productivity. The purpose of this study is thus to classify the factors that affect the productivity of ceramic tiling crews by using data mining methods. To achieve the purpose of our study, a systematic time study was undertaken with ceramic tiling crews in Turkey. Daily productivity values of ceramic tiling crews were collected together with the information related with the factors like the crew size, age and experience of crewmembers. Collected data was classified by using Weka program. The outlier values were first removed from the dataset and decision tree method was used to classify the new dataset. Decision tree method was preferred due to its easiness of use and rapidness in classification. Apriori algorithm, which is the mostly preferred association algorithm in previous studies, was also used to highlight the general trend in the dataset. (C) 2014 The Authors. Published by Elsevier Ltd.
Construction Crew Productivity Prediction By Using Data Mining Methods
(Elsevier Science Bv, 2014) Kaya, Mumine; Keles, Abdullah Emre; Oral, Emel Laptali
Ceramic tiling industry has become one of Turkey's fastest growing industries due to the outstanding achievements of Turkish ceramic producers with respect to producing high quality products with lower costs than their equivalents worldwide. Conversely high costs of the end product of Turkish building industry in general show that there is an important problem with the productivity and quality of construction crews. That's why most construction firms begin to realize the need for a detailed research on the factors affecting construction crew productivity. The purpose of this study is thus to classify the factors that affect the productivity of ceramic tiling crews by using data mining methods. To achieve the purpose of our study, a systematic time study was undertaken with ceramic tiling crews in Turkey. Daily productivity values of ceramic tiling crews were collected together with the information related with the factors like the crew size, age and experience of crewmembers. Collected data was classified by using Weka program. The outlier values were first removed from the dataset and decision tree method was used to classify the new dataset. Decision tree method was preferred due to its easiness of use and rapidness in classification. Apriori algorithm, which is the mostly preferred association algorithm in previous studies, was also used to highlight the general trend in the dataset. (C) 2014 The Authors. Published by Elsevier Ltd.
Determination of Green Building Awareness: A Study in Turkey
(Mdpi, 2022) Keles, Abdullah Emre; Onen, Ecem; Gorecki, Jaroslaw
The building sector is the world's most significant energy consumer. In addition to that, water consumption and increased waste are some of the most significant issues. Owing to the need to find a solution to this problem, the concept of green buildings has emerged. Green buildings are building types that consume less energy and are constructed with recyclable materials, in harmony with nature. The adoption of the concept of green building in societies is very important in this regard. This study aimed to understand the awareness level of people about green buildings. Its scope was to determine the level of awareness of people living in buildings with and without an energy identity certificate in Adana. The results were created in Microsoft Excel, and the survey questions were measured using SPSS. Data analysis was performed by the WEKA tool using the association rule mining method. According to the result, most of the participants did not have sufficient information about the subjects. The results show that nowadays, most people do not understand this building type and what it means to the next generation.
DISTANCE EDUCATION WITH MOODLE IN ENGINEERING EDUCATION: ONLINE PROGRAMMING ASSIGNMENTS COMPILATION
(Univ North, 2018) Kaya Keles, Mumine; Keles, Abdullah Emre
The concept of distance education systems is a concept that applies to all levels of education, including universities. The use of distance education systems has increased considerably in universities today. Many faculties in many universities use distance education systems for their courses. The purpose of this paper is to design and develop a system that can be used to upload lecture notes and assignments online via the Internet, to do online exams, to provide a compilation control of all the assignments written, especially in the C programming language, by instructors who are primarily in the Engineering Department, then all instructors in the universities using the Moodle platform. Moreover, the aim of this paper is to design and develop a system in which the students primarily in the Engineering Department using the Moodle platform and then students in all the universities can follow the course contents, upload the assignments, and discuss their questions about the course with their instructors and their friends. As a result of this paper, a scheme is provided to easily compile, run and grade the programming assignments (source codes) given in the Programming courses using the Moodle website collected in a single place.
DISTANCE EDUCATION WITH MOODLE IN ENGINEERING EDUCATION: ONLINE PROGRAMMING ASSIGNMENTS COMPILATION
(Univ North, 2018) Kaya Keles, Mumine; Keles, Abdullah Emre
The concept of distance education systems is a concept that applies to all levels of education, including universities. The use of distance education systems has increased considerably in universities today. Many faculties in many universities use distance education systems for their courses. The purpose of this paper is to design and develop a system that can be used to upload lecture notes and assignments online via the Internet, to do online exams, to provide a compilation control of all the assignments written, especially in the C programming language, by instructors who are primarily in the Engineering Department, then all instructors in the universities using the Moodle platform. Moreover, the aim of this paper is to design and develop a system in which the students primarily in the Engineering Department using the Moodle platform and then students in all the universities can follow the course contents, upload the assignments, and discuss their questions about the course with their instructors and their friends. As a result of this paper, a scheme is provided to easily compile, run and grade the programming assignments (source codes) given in the Programming courses using the Moodle website collected in a single place.
Investigation of Lean Production Knowledge among Employees in Building Inspection Organizations
(Mdpi, 2022) Keles, Abdullah Emre; Yilmaz, Hatice Kubra
Currently, lean construction has become very important, with lean manufacturing, a philosophy of production that in the most basic sense refers to the elimination of waste and tries to eliminate anything that does not add value to the product or service, emerging as a Japanese business pattern. The aim of this study was to determine the lean production knowledge among employees in building inspection organizations. To measure the level of awareness in construction inspection institutions, which are the leading entities in the construction sector, and the level of awareness on this issue, a survey was conducted through face-to-face meetings with engineering employees residing in Adana province. The results were converted into datasets in the appropriate Microsoft Excel format. With these datasets, the reliability level of the survey was measured using the SPSS program, and the data were analyzed using WEKA software. The study used association rule learning, a data mining method. According to the results, the number of employees who knew the definition of lean production was less than half, at 47%, while the number of those who knew about the concept of lean construction was 44%. The results obtained according to three different class labels were statistically interpreted, and it was determined that the employees lacked knowledge and sufficient awareness about the subject.
Make saving crucial again: building energy efficiency awareness of people living in urban areas
(Taylor & Francis Ltd, 2022) Keles, Abdullah Emre; Onen, Ecem; Gorecki, Jaroslaw
Construction is one of the most energy-intensive sectors in the world. To scale down the energy demand of the building sector, some changes must be made. Formal exemplifications of this need can be seen in recent changes in the law in different countries. The energy identity/performance certificate contains requirements about buildings' energy consumption in Turkey, and the Energy Performance Regulation in Buildings is mandatory from 01.01.2020. Moreover, it aimed to measure the level of awareness of individuals in saving energy. Face-to-face surveys were conducted with the use of a questionnaire with individuals residing in Adana's pilot region on the awareness of similar issues such as green buildings and energy efficiency, especially energy identity/performance certificate. The survey results were prepared in Microsoft Excel, and the reliability of the survey questions was measured with the help of the SPSS (Statistical Package for the Social Sciences) program. The analysis of the data was obtained from WEKA (Waikato Environment for Knowledge Analysis). Association rule extraction, which is one of the data mining methods, was used in the analysis. Based on the findings, it was seen that most of the individuals did not have enough information about the topics in the survey.
Overview of Environmental Problems Caused by Logistics Transportation: Example of European Union Countries
(Univ North, 2021) Keles, Abdullah Emre; Gungor, Gokhan
Logistic transportation works is considered the main pollutant for the environment, with over 25 per cent CO2 emissions in the EU. This situation has raised concerns for the EU and governments to find solutions and impose regulations to diminish the environmental impact. The purpose of this paper is to investigate two questions regarding environmental issues connected to transportation; What are the recent trends for establishing sustainable logistics for different transportation methods? Which alternatives can be incentivized to decrease the environmental impact of transportation activities? These projects among the most noteworthy environmental benefits, which are also the case studies of this research are The Viking Concept, The WestMed Bridge, Scandinavian Shuttle and Baxter Inland Sea Transport. The result highlighted in this research is that shifting transportation modes has been successfully implemented, with considerable positive outcomes in terms of environment impact, cost and lead times in several European Union funded projects.
PREDICTION OF CONCRETE STRENGTH WITH DATA MINING METHODS USING ARTIFICIAL BEE COLONY AS FEATURE SELECTOR
(IEEE, 2018) Kaya Keles, Mumine; Keles, Abdullah Emre; Kılıç, Umit
Concrete which is a highly complex material is the most basic input of the construction industry. Because of its strength, concrete is one of the most preferred structural building materials. In the ready-mixed concrete sector, there is an increasing need for earthquake resistant structures due to the fact that some producers produce out of control and poor quality. Ready-mixed concrete is a product whose quality can only be understood at the end of the 28th day if it is only controlled by taking the sample by the user. In this study, a data mining study was conducted on the factors affecting the 28-day compressive strength of concrete using the Concrete Slump Test Data Set from UCI Machine Learning Repository. The Artificial Bee Colony Algorithm is used as a feature selection method in order to determine the important ones of the concrete components, which are cement, slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate, affecting concrete strength and tried to predict the strength with data mining algorithms. As a result of the study, it was observed that Random Forest Algorithm gave the highest success rate with 91.2621% accuracy using only 3 features, which are cement, fly ash, and water. This means that it is possible to predict the compressive strength of concrete with a ratio above 90% by using a smaller number of concrete components.
PREDICTION OF CONCRETE STRENGTH WITH DATA MINING METHODS USING ARTIFICIAL BEE COLONY AS FEATURE SELECTOR
(IEEE, 2018) Kaya Keles, Mumine; Keles, Abdullah Emre; Kilic, Umit
Concrete which is a highly complex material is the most basic input of the construction industry. Because of its strength, concrete is one of the most preferred structural building materials. In the ready-mixed concrete sector, there is an increasing need for earthquake resistant structures due to the fact that some producers produce out of control and poor quality. Ready-mixed concrete is a product whose quality can only be understood at the end of the 28th day if it is only controlled by taking the sample by the user. In this study, a data mining study was conducted on the factors affecting the 28-day compressive strength of concrete using the Concrete Slump Test Data Set from UCI Machine Learning Repository. The Artificial Bee Colony Algorithm is used as a feature selection method in order to determine the important ones of the concrete components, which are cement, slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate, affecting concrete strength and tried to predict the strength with data mining algorithms. As a result of the study, it was observed that Random Forest Algorithm gave the highest success rate with 91.2621% accuracy using only 3 features, which are cement, fly ash, and water. This means that it is possible to predict the compressive strength of concrete with a ratio above 90% by using a smaller number of concrete components.
Proposed Artificial Bee Colony Algorithm as Feature Selector to Predict the Leadership Perception of Site Managers
(Oxford Univ Press, 2021) Keles, Mumine Kaya; Kılıç, Umit; Keles, Abdullah Emre
Datasets have relevant and irrelevant features whose evaluations are fundamental for classification or clustering processes. The effects of these relevant features make classification accuracy more accurate and stable. At this point, optimization methods are used for feature selection process. This process is a feature reduction process finding the most relevant feature subset without decrement of the accuracy rate obtained by original feature sets. Varied nature inspiration-based optimization algorithms have been proposed as feature selector. The density of data in construction projects and the inability of extracting these data cause various losses in field studies. In this respect, the behaviors of leaders are important in the selection and efficient use of these data. The objective of this study is implementing Artificial Bee Colony (ABC) algorithm as a feature selection method to predict the leadership perception of the construction employees. When Random Forest, Sequential Minimal Optimization and K-Nearest Neighborhood (KNN) are used as classifier, 84.1584% as highest accuracy result and 0.805 as highest F-Measure result were obtained by using KNN and Random Forest classifier with proposed ABC Algorithm as feature selector. The results show that a nature inspiration-based optimization algorithm like ABC algorithm as feature selector is satisfactory in prediction of the Construction Employee's Leadership Perception.
Proposed Artificial Bee Colony Algorithm as Feature Selector to Predict the Leadership Perception of Site Managers
(Oxford Univ Press, 2021) Keles, Mumine Kaya; Kilic, Umit; Keles, Abdullah Emre
Datasets have relevant and irrelevant features whose evaluations are fundamental for classification or clustering processes. The effects of these relevant features make classification accuracy more accurate and stable. At this point, optimization methods are used for feature selection process. This process is a feature reduction process finding the most relevant feature subset without decrement of the accuracy rate obtained by original feature sets. Varied nature inspiration-based optimization algorithms have been proposed as feature selector. The density of data in construction projects and the inability of extracting these data cause various losses in field studies. In this respect, the behaviors of leaders are important in the selection and efficient use of these data. The objective of this study is implementing Artificial Bee Colony (ABC) algorithm as a feature selection method to predict the leadership perception of the construction employees. When Random Forest, Sequential Minimal Optimization and K-Nearest Neighborhood (KNN) are used as classifier, 84.1584% as highest accuracy result and 0.805 as highest F-Measure result were obtained by using KNN and Random Forest classifier with proposed ABC Algorithm as feature selector. The results show that a nature inspiration-based optimization algorithm like ABC algorithm as feature selector is satisfactory in prediction of the Construction Employee's Leadership Perception.
The Effect of Adaptive Neuro-fuzzy Inference System (ANFIS) on Determining the Leadership Perceptions of Construction Employees
(Springer Int Publ Ag, 2023) Keles, Abdullah Emre; Haznedar, Bulent; Keles, Muemine Kaya; Arslan, Mustafa Turan
In the construction industry, which is Turkey's locomotive and the strategic sector, determining the kind of leadership that impacts employees' productivity is directly related to the success of the business. The identification of leadership types that will motivate and support employees has great importance in terms of construction businesses where the human element is at the forefront. From the point of view of the site chiefs, it is thought that it will benefit all the stakeholders in the construction sector to determine which leader type will motivate which employees. In this study, the productivity relations between the engineers working in construction companies constructing buildings in Adana Province and the employees who are the hierarchically lower-level employees of these persons were investigated using bi-directional surveys. The impact of leadership types on the employees' productivity has been investigated using machine learning. The effects of ANFIS method and the use of genetic algorithm (GA) on the training of ANFIS for the classification are investigated. The data set, which was prepared within the scope of the study, was classified by ANFIS-genetic algorithm (ANFIS-GA), ANFIS-backpropagation algorithm (ANFIS-BP), and ANFIS-hybrid algorithm (ANFIS-HB) algorithms after the required preprocesses. The 10-fold cross-validation technique is used to test the performance of the classification methods. According to the obtained results, the highest accuracy rate of 82.18% is obtained when ANFIS-GA algorithm is used as a classifier. As a result of the study, it is concluded that for this data set, ANFIS, an artificial neural network-based algorithm, is more successful in determining the leadership perceptions of construction employees when it is trained by GA.
The Effect of Adaptive Neuro-fuzzy Inference System (ANFIS) on Determining the Leadership Perceptions of Construction Employees
(Springer Int Publ Ag, 2023) Keles, Abdullah Emre; Haznedar, Bulent; Keles, Muemine Kaya; Arslan, Mustafa Turan
In the construction industry, which is Turkey's locomotive and the strategic sector, determining the kind of leadership that impacts employees' productivity is directly related to the success of the business. The identification of leadership types that will motivate and support employees has great importance in terms of construction businesses where the human element is at the forefront. From the point of view of the site chiefs, it is thought that it will benefit all the stakeholders in the construction sector to determine which leader type will motivate which employees. In this study, the productivity relations between the engineers working in construction companies constructing buildings in Adana Province and the employees who are the hierarchically lower-level employees of these persons were investigated using bi-directional surveys. The impact of leadership types on the employees' productivity has been investigated using machine learning. The effects of ANFIS method and the use of genetic algorithm (GA) on the training of ANFIS for the classification are investigated. The data set, which was prepared within the scope of the study, was classified by ANFIS-genetic algorithm (ANFIS-GA), ANFIS-backpropagation algorithm (ANFIS-BP), and ANFIS-hybrid algorithm (ANFIS-HB) algorithms after the required preprocesses. The 10-fold cross-validation technique is used to test the performance of the classification methods. According to the obtained results, the highest accuracy rate of 82.18% is obtained when ANFIS-GA algorithm is used as a classifier. As a result of the study, it is concluded that for this data set, ANFIS, an artificial neural network-based algorithm, is more successful in determining the leadership perceptions of construction employees when it is trained by GA.
The Investigation of the Applicability of Data-Driven Techniques in Hydrological Modeling: The Case of Seyhan Basin
(Middle Pomeranian Sci Soc Env Prot, 2019) Turhan, Evren; Keles, Mumine Kaya; Tantekin, Atakan; Keles, Abdullah Emre
Proper water resources planning and management is based on reliable hydrological data. Missing rainfall and runoff observation data, in particular, can cause serious risks in the planning of hydraulics structures. Hydrological modeling process is quitely complex. Therefore, using alternative estimation techniques to forecast missing data is reasonable. In this study, two data-driven techniques such as Artificial Neural Networks (ANN) and Data Mining were investigated in terms of availability in hydrology works. Feed Forward Back Propagation (FFBPNN) and Generalized Regression Neural Networks (GRNN) methods were performed on rainfall-runoff modeling for ANN. Besides, Hydrological drought analysis were examined using data mining technique. The Seyhan Basin was preferred to carry out these techniques. It is thought that the application of different techniques in the same basin could make a great contribute to the present work. Consequently, it is seen that FFBPNN is the best model for ANN in terms of giving the highest R2 and lowest MSE values. Multilayer Perceptron (MLP) algorithm was used to predict the drought type according to limit values. This system has been applied to show the relationship between hydrological data and measure the prediction accuracy of the drought analysis. According to the obtained data mining results, MLP algorithm gives the best accuracy results as flow observation stations using SRI-3 month data.
The Investigation of the Applicability of Data-Driven Techniques in Hydrological Modeling: The Case of Seyhan Basin
(Middle Pomeranian Sci Soc Env Prot, 2019) Turhan, Evren; Keles, Mumine Kaya; Tantekin, Atakan; Keles, Abdullah Emre
Proper water resources planning and management is based on reliable hydrological data. Missing rainfall and runoff observation data, in particular, can cause serious risks in the planning of hydraulics structures. Hydrological modeling process is quitely complex. Therefore, using alternative estimation techniques to forecast missing data is reasonable. In this study, two data-driven techniques such as Artificial Neural Networks (ANN) and Data Mining were investigated in terms of availability in hydrology works. Feed Forward Back Propagation (FFBPNN) and Generalized Regression Neural Networks (GRNN) methods were performed on rainfall-runoff modeling for ANN. Besides, Hydrological drought analysis were examined using data mining technique. The Seyhan Basin was preferred to carry out these techniques. It is thought that the application of different techniques in the same basin could make a great contribute to the present work. Consequently, it is seen that FFBPNN is the best model for ANN in terms of giving the highest R2 and lowest MSE values. Multilayer Perceptron (MLP) algorithm was used to predict the drought type according to limit values. This system has been applied to show the relationship between hydrological data and measure the prediction accuracy of the drought analysis. According to the obtained data mining results, MLP algorithm gives the best accuracy results as flow observation stations using SRI-3 month data.

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