Groutability Estimation of Grouting Processes with Microfine Cements Using an Evolutionary Instance-Based Learning Approach

AbstractIn the construction industry, estimating groutability is a crucial task in the planning phase of a grouting project. Hence, establishing an effective groutability prediction model that is simple to implement and can deliver quick responses with high accuracy is a practical need of construction engineers. In this research, a novel instance-based learning approach—Evolutionary Fuzzy k-Nearest Neighbor Inference Model (EFKNIM)—is proposed to forecast the result of grouting activities that employ microfine cement grouts. In the proposed model, the fuzzy k-nearest neighbor algorithm is used to classify grouting activities into two classes: success and failure. Meanwhile, the differential evolution optimization approach is deployed to select the most appropriate tuning parameters of the fuzzy k-nearest neighbor algorithm, namely the neighboring size (k) and the fuzzy strength (m). This integrated framework allows the EFKNIM to operate autonomously without human prior knowledge or tedious processes for p...

[1]  C. Vipulanandan,et al.  Effect of Grain Size and Distribution on Permeability and Mechanical Behavior of Acrylamide Grouted Sand , 2005 .

[2]  Christian Butron,et al.  Drip sealing of tunnels in hard rock: A new concept for the design and evaluation of permeation grouting , 2010 .

[3]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[4]  A. Sağlamer,et al.  Estimating the groutability of granular soils: a new approach , 2002 .

[5]  Gang Wang,et al.  A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method , 2011, Knowl. Based Syst..

[6]  Gang Wang,et al.  An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach , 2013, Expert Syst. Appl..

[7]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[8]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[9]  Sheng-Tun Li,et al.  Predicting financial activity with evolutionary fuzzy case-based reasoning , 2009, Expert Syst. Appl..

[10]  R. M. Chandrasekaran,et al.  Evaluation of k-Nearest Neighbor classifier performance for direct marketing , 2010, Expert Syst. Appl..

[11]  Swagatam Das,et al.  Automatic Clustering Using an Improved Differential Evolution Algorithm , 2007 .

[12]  Moncef Gabbouj,et al.  Evolutionary artificial neural networks by multi-dimensional particle swarm optimization , 2009, Neural Networks.

[13]  C. Coello,et al.  Cultured differential evolution for constrained optimization , 2006 .

[14]  Antonin Ponsich,et al.  Differential Evolution performances for the solution of mixed-integer constrained process engineering problems , 2011, Appl. Soft Comput..

[15]  Sami O. Akbas,et al.  Artificial neural networks approach for estimating the groutability of granular soils with cement-based grouts , 2011 .

[16]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[18]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[19]  Raymond J. Krizek,et al.  Injection of Fine Sands with Very Fine Cement Grout , 1989 .

[20]  John Kelly,et al.  Artificial intelligence - a modern myth , 1993, Ellis Horwood series in artificial intelligence.

[21]  Carlos A. Coello Coello,et al.  Simple Feasibility Rules and Differential Evolution for Constrained Optimization , 2004, MICAI.

[22]  Hassan Al Nageim,et al.  Comparison of neural network and binary logistic regression methods in conceptual design of tall steel buildings , 2007 .

[23]  Ping Zhang.  Model Selection Via Multifold Cross Validation , 1993 .

[24]  S. Perret,et al.  Repair of 130-Year Old Masonry Bridge using High-Performance Cement Grout , 2002 .

[25]  Chien-Lin Huang,et al.  An artificial neural network for groutability prediction of permeation grouting with microfine cement grouts , 2011 .

[26]  A. Ozdemir.  Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey) , 2011 .

[27]  H. A. Nefeslioglu,et al.  An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps , 2008 .

[28]  Sandhya Samarasinghe,et al.  Neural Networks for Applied Sciences and Engineering , 2006 .

[29]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[30]  M. Eeckhaut,et al.  Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium) , 2006 .