Abstract
This paper presents a fuzzy subtractive modelling technique to predict the weight of telecommunication towers which is used to estimate their respective costs. This is implemented through the utilization of data from previously installed telecommunication towers considering four input parameters: a) tower height; b) allowed tilt or deflection; c) antenna subjected area loading; and d) wind load. Telecommunication towers are classified according to designated code (TIA-222-F and TIA-222-G standards) and structures type (Self-Supporting Tower (SST) and Roof Top (RT)). As such, four fuzzy subtractive models are developed to represent the four classes. To build the fuzzy models, 90% of data are utilized and fed to Matlab software as training data. The remaining 10% of the data are utilized to test model performance. Sugeno-Type first order is used to optimize model performance in predicting tower weights. Errors are estimated using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) for both training and testing data sets. Sensitivity analysis is carried to validate the model and observe the effect of clusters’ radius on models performance.
Highlights
Modelling in construction proved its effect on predicting data
This paper presented a fuzzy subtractive modelling technique to predict the weight of telecommunication towers which is used to estimate their respective costs
Four input parameters are considered in the modelling: tower height, allowed tilt or deflection, antenna subjected area loading, and wind load
Summary
Modelling in construction proved its effect on predicting data. Many models were developed with some mathematical operations to guide practitioners with the expected scenario that most likely take place during construction phase. 1998; Hegazy, Ayed 1998; Creese, Li 1995) for different construction projects such as highways, bridges, and low-rise structural steel buildings These modelling techniques utilize several approaches including multivariate regression (Fragkakis et al 2011; Trost, Oberlender 2003), neural networks (Yu, Skibniewski 2010; Cheng et al 2009; Siqueira 1998; Hegazy, Ayed 1998; Creese, Li 1995), and Case-Based Reasoning (Jin et al 2012; Ji et al 2012; Marzouk, Ahmed 2011; Chou 2009; Wang et al 2008). The proposed subtractive models act as a decision support tool that can be used to predict the costs of telecommunication towers projects at acceptable accuracy level once their specifications are set
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