Analysis of Cost and Schedule Variances in Construction Works with Artificial Intelligence Approaches: The Case of Turkey

Realistic estimation of construction cost is a vital issue for both successful planningand completion of every construction project. However, fluctuations in input prices due to the unexpected changes in factors like inflation and supply/demand balance make realistic costestimation very difficult to achieve. Thus, various estimation methods have been developedand these can be grouped as methods based on; statistics-probability analysis, comparison with similar projects and artificial intelligence techniques.Statistics-probability analysis is the most widely used method for construction costestimation in Turkey. Based on the so called method, Ministry of the Environment and Urbanism publishes and updates "Unit Costs of Construction" every year and the data is widely used for preliminary cost estimation by both the contractors and the developers.Meanwhile, methods based on artificial intelligence techniques are rarely used within the industry. Thus, the aim of this study has been to compare the estimation results obtained by using statistics-probability analysis and artificial intelligent techniques. In order to achieve this, construction cost data from 198 projects; completed between 2004-2010 in Izmir (the third largest city in Turkey) were used. Multi layer perceptron (MLP) and grid partitioning algorithm (GPA) were used to obtain estimation results and root mean square error (RMSE)and coefficient of determination (R2) were calculated for comparisons

Comparison of multi layer perceptron (MLP) and radial basis function (RBF) for construction cost estimation: the case of Turkey

In Turkey, for the preliminary construction cost estimation, a notice, which is updated and published annu­ally by Turkish Ministry of the Environment and Urbanism, known as “unit area cost method” (UACM) is generally employed. However, it’s known that the costs obtained through this method in which only construction area is taken into consideration have significant differences from actual costs. The aim of this study is to compare the cost estimations obtained through “multi layer perceptron” (MLP) and “radial basis function” (RBF), which are commonly used artificial neural network (ANN) methods. The results of MLP and RBF were also compared with the results of UACM and the validity of UACM was interpreted. Dataobtained from 232 public construction projects, which completed between 2003 and 2011 in different regions of Turkey, were reviewed. Consequently, estimated costs obtained from RBF were found to be higher than the actual costs with a 0.28% variance, while the estimated costs obtained from MLP were higher than actual values with a 1.11% variance. The approximate costs obtained from UACM are higher than actual costs with a 28.73% variance. It was found that both ANN methods were showed better performance than the UACM but RBF was superior to MLP. First published online: 24 Aug 201

Construction Crew Productivity Prediction By Using Data Mining Methods

4th World Conference on Learning, Teaching and Educational Leadership (WCLTA) -- OCT 27-29, 2013 -- Univ Barcelona, Barcelona, SPAINWOS: 000345351800205Ceramic 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

Industry financial ratios - application of factor analysis in Turkish construction industry

WOS: 000241590600040Turkish economy has been hit by various economical crises between the years 1998 and 2001 and the economic stagnation still continues. Past experiences in various countries show that it is vitally important to encourage construction activities in order to get out of stagnation, as construction output directly affects about 200 other sectors and industry financial ratio analysis is a means to provide a basis for the governments to undertake corrective action. However, there are over 50 financial ratios that can be used during analysis and some are more important than the others for different industries. Previous research has shown that there are about 25 factors that are important for the construction companies. This, in turn, requires elimination of unrelated data. Factor analysis is a data reduction and classification technique, which can be applied in financial analysis. Factor analysis was thus applied to the financial data collected from Turkish construction companies for a 5-year period in order to determine the financial indicators that can be used to analyse the financial trend of the industry. Five independent factors, i.e. liquidity, capital structure and profitability, activity efficiency, profit margin and growth, and assets structure were identified to be sensitive to the economical changes in the country. The results of the factor-based analysis can be used both by the government to analyse the changes in the industry with respect to time and by the construction companies to analyse their financial state with respect to their rivals. (c) 2005 Elsevier Ltd. All rights reserved

Comparison of Unit Price Method and Unit Area Cost Method for Construction Cost Estimation

In the construction industry, increasing competition environment has led to decrease in the profit share of the projects. Accordingly, importance of construction cost estimation works has been increased for both the employer and the contractor. The purpose of this study has been to determine and compare the construction cost estimations obtained by widely used databases, "unit price method" (UPM) and "unit area cost method" (UACM) in Turkey. For this purpose, construction data from 420 projects, which were procured in accordance with the Public Tender Law no 4734 by the Turkish Ministry of Environment and Urbanism and completed between 2003 and 2011, were reviewed. Root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R-2) were calculated for the comparison of actual and estimated cost values. Consequently, RMSE and MAPE values from UPM were underestimated with the ratio of 13.57% and 1.73% than UACM. Although UPM showed better performance than the UACM, it is not at a satisfactory level