Feature Extraction and Selection in Ground Penetrating Radar with Experimental Data Set of Inclusions in Concrete Blocks

Ground Penetrating Radar systems have been successfully used to access concrete structures conditions. Moreover, inclusions in concrete can be discriminated by simple models based on traces obtained by GPR. In this work, concrete blocks with different inclusions were probed in controlled conditions. Some features were extracted from Ascans of this experimental data set. To get efficient models, raw data were submitted to features selection and space reduction methods. Without complex data pre-processing, good accuracy and more explainable models with less computational burden were obtained.

[1]  Ayse Basar Bener,et al.  Exploiting the Essential Assumptions of Analogy-Based Effort Estimation , 2012, IEEE Transactions on Software Engineering.

[2]  David Baccarini,et al.  The concept of project complexity—a review , 1996 .

[3]  D. Daniels Ground Penetrating Radar , 2005 .

[4]  Magne Jørgensen,et al.  Estimating Software Development Effort Based on Use Cases-Experiences from Industry , 2001, UML.

[5]  Satyananda Reddy,et al.  A New Approach For Estimating Software Effort Using RBFN Network , 2008 .

[6]  Guangyin Lu,et al.  Review of GPR Rebar Detection , 2009 .

[7]  Ali Bou Nassif,et al.  Software Size and Effort Estimation from Use Case Diagrams Using Regression and Soft Computing Models , 2012 .

[8]  Danny Ho,et al.  Estimating Software Effort Based on Use Case Point Model Using Sugeno Fuzzy Inference System , 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

[9]  Lu Chen,et al.  Extended Use Case Points Method for Software Cost Estimation , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[10]  Danny Ho,et al.  Software Effort Estimation in the Early Stages of the Software Life Cycle Using a Cascade Correlation Neural Network Model , 2012, 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing.

[11]  A. Nicolas,et al.  In the Use of Parametric and Non Parametric Algorithms for the Non Destructive Evaluation of Concrete Structures , 2009 .

[12]  Xiongyao Xie,et al.  A fuzzy comprehensive evaluation system of mountain tunnel lining based on the fast nondestructive inspection , 2011, 2011 International Conference on Remote Sensing, Environment and Transportation Engineering.

[13]  Gustav Karner,et al.  Resource Estimation for Objectory Projects , 2010 .

[14]  Martin Arnold,et al.  Software size measurement and productivity rating in a large-scale software development department , 1998, Proceedings of the 20th International Conference on Software Engineering.

[15]  Ayse Basar Bener,et al.  Ensemble of neural networks with associative memory (ENNA) for estimating software development costs , 2009, Knowl. Based Syst..

[16]  Salvatore Caorsi,et al.  An electromagnetic approach based on neural networks for the GPR investigation of buried cylinders , 2005, IEEE Geoscience and Remote Sensing Letters.

[17]  Gabriela Robiolo,et al.  Employing use cases to early estimate effort with simpler metrics , 2007, Innovations in Systems and Software Engineering.

[18]  Joaquín Izquierdo,et al.  Location of buried plastic pipes using multi-agent support based on GPR images , 2011 .

[19]  Imad L. Al-Qadi,et al.  Dielectric Properties of Portland Cement Concrete at Low Radio Frequencies , 1995 .

[20]  Danny Ho,et al.  Regression Model for Software Effort Estimation Based on the Use Case Point Method , 2011 .

[21]  James H. Garrett,et al.  Ground-penetrating radar for highway and bridge deck condition assessment and inventory , 1995, Smart Structures.

[22]  R. Conradi,et al.  Effort estimation of use cases for incremental large-scale software development , 2005, Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005..

[23]  Miroslaw Ochodek,et al.  Automatic Transactions Identification in Use Cases , 2008, CEE-SET.

[24]  M. Møller A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1990 .

[25]  Masoud Nikravesh,et al.  Feature Extraction - Foundations and Applications , 2006, Feature Extraction.

[26]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[27]  Peter I. Cowling,et al.  Analogy-based software effort estimation using Fuzzy numbers , 2011, J. Syst. Softw..

[28]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[29]  Sergey Diev Use cases modeling and software estimation: applying use case points , 2006, SOEN.

[30]  Pierre-Claude Aitcin,et al.  Concrete structure, properties and materials , 1986 .

[31]  Douglas A. G. Vieira,et al.  Analyzing the Relevant Features of GPR Scattered Waves in Time- and Frequency-Domain , 2013 .

[33]  G. Arliguie,et al.  Non-destructive evaluation of concrete physical condition using radar and artificial neural networks , 2009 .

[34]  Danny Ho,et al.  Towards an early software estimation using log-linear regression and a multilayer perceptron model , 2013, J. Syst. Softw..

[35]  Ali Idri,et al.  Design of Radial Basis Function Neural Networks for Software Effort Estimation , 2010 .

[36]  Abbas Heiat,et al.  Comparison of artificial neural network and regression models for estimating software development effort , 2002, Inf. Softw. Technol..

[37]  Steve Millard,et al.  Location of steel reinforcement in concrete using ground penetrating radar and neural networks , 2005 .

[38]  Ayse Basar Bener,et al.  Feature weighting heuristics for analogy-based effort estimation models , 2009, Expert Syst. Appl..

[39]  Kasi Periyasamy,et al.  Cost Estimation Using Extended Use Case Point (e-UCP) Model , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[40]  Harald Richter,et al.  A component-based specification approach for embedded systems using FDTs , 2006 .

[41]  Cuauhtémoc López Martín,et al.  Software development effort prediction of industrial projects applying a general regression neural network , 2011, Empirical Software Engineering.

[42]  Oral Buyukozturk,et al.  A methodology for determining complex permittivity of construction materials based on transmission-only coherent, wide-bandwidth free-space measurements , 2006 .

[43]  Parag C. Pendharkar,et al.  A probabilistic model for predicting software development effort , 2003, IEEE Transactions on Software Engineering.

[44]  J. H. Bungey,et al.  SUB-SURFACE RADAR TESTING OF CONCRETE: A REVIEW , 2004 .

[45]  Gabriela Robiolo,et al.  Transactions and paths: Two use case based metrics which improve the early effort estimation , 2009, ESEM 2009.

[46]  Ikhlas Abdel-Qader,et al.  Fractals and independent component analysis for defect detection in bridge decks , 2011 .

[47]  Miroslaw Ochodek,et al.  Simplifying effort estimation based on Use Case Points , 2011, Inf. Softw. Technol..

[48]  Silvia Regina Vergilio,et al.  Software Effort Estimation Based on Use Cases , 2006, 30th Annual International Computer Software and Applications Conference (COMPSAC'06).

[49]  J. H. Bungey,et al.  Radar assessment of structural concrete using neural networks , 1995 .