The knowledge modeling system of ready-mixed concrete enterprise and artificial intelligence with ANN-GA for manufacturing production

Based on the characteristics of ready-mixed concrete enterprises, this paper puts forward that knowledge management (KM) is an effective way to contribute to enterprise production and operation. The knowledge content and relevant models of concrete enterprises are proposed, including advanced enterprise management, decision support for production operation, production and operation cost, and marketing-customer relationship. Afterwards knowledge contents are divided into static, strategic and reasoning knowledge. Besides knowledge unified expression is put forward accordingly. In addition, the KM system for process ready-mixed concrete enterprises management is established to facilitate effective production processing. As part of exploratory study, artificial neural network coupled with genetic algorithm (ANN-GA) as knowledge mining technology is applied in KM system to predict the 28-day compressive strength in concrete enterprises. The results shows that compared to back-propagation artificial neural network, the convergence rate of ANN-GA algorithm has been significantly improved and almost all the relative errors of predicted compressive strength of concrete C30 are within 3 %. It not only confirms the validity of the models, but also proves that ANN-GA algorithm is an effective knowledge mining method applied in concrete industry.

[1]  Manish A. Kewalramani,et al.  Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks , 2006 .

[2]  Edmundas Kazimieras Zavadskas,et al.  Integrated knowledge management model and system for construction projects , 2010, Eng. Appl. Artif. Intell..

[3]  Bhargav Dave,et al.  Collaborative knowledge management—A construction case study , 2009 .

[4]  Jui-Sheng Chou,et al.  Concrete compressive strength analysis using a combined classification and regression technique , 2012 .

[5]  Habibollah Haron,et al.  Genetic Algorithm and Simulated Annealing to estimate optimal process parameters of the abrasive waterjet machining , 2011, Engineering with Computers.

[6]  H. Raheman,et al.  Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA , 2009 .

[7]  María del Mar Fuentes-Fuentes,et al.  Knowledge combination, innovation, organizational performance in technology firms , 2013, Ind. Manag. Data Syst..

[8]  Okan Karahan,et al.  Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network , 2009, Adv. Eng. Softw..

[9]  Camelia Chira,et al.  An agent-based approach to knowledge management in distributed design , 2006, J. Intell. Manuf..

[10]  Sandro Wartzack,et al.  Neural network based modeling and optimization of deep drawing – extrusion combined process , 2014, J. Intell. Manuf..

[11]  Cong-Dong Li,et al.  GMVN oriented S-BOX knowledge expression and reasoning framework , 2014, J. Intell. Manuf..

[12]  J. Sobhani,et al.  Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models , 2010 .

[13]  She-I Chang,et al.  Information systems integration after merger and acquisition , 2014, Ind. Manag. Data Syst..

[14]  Antonio Mihi Ramírez,et al.  Knowledge creation and flexibility of distribution of information , 2012 .

[15]  Ángel L. Meroño-Cerdán,et al.  International Journal of Information Management Strategic Knowledge Management, Innovation and Performance , 2022 .

[16]  Solomon Tesfamariam,et al.  Adaptive Network — Fuzzy Inferencing to Estimate Concrete Strength Using Mix Design , 2007 .

[17]  Qian Li,et al.  Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method , 2007 .

[18]  Zhenyu Huang,et al.  Rethinking ERP success: A new perspective from knowledge management and continuous improvement , 2007, Inf. Manag..

[19]  Xu Ji,et al.  Study of the Knowledge–Based Integrated Equipment Management System for Process Enterprise , 2011 .

[20]  Dilanthi Amaratunga,et al.  A theoretical framework on managing tacit knowledge for enhancing performance in the construction industry , 2006 .

[21]  Wladimir Bodrow Toward European knowledge enterprises , 2007, J. Intell. Manuf..

[22]  Siti Zaiton Mohd Hashim,et al.  Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007-2011) , 2012, Expert Syst. Appl..

[23]  Muhammad Fauzi Mohd. Zain,et al.  Multiple regression model for compressive strength prediction of high performance concrete , 2009 .

[24]  Diyi Chen,et al.  Application of Takagi–Sugeno fuzzy model to a class of chaotic synchronization and anti-synchronization , 2013 .

[25]  Wei Chen,et al.  Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal , 2010 .

[26]  Mehdi Nikoo,et al.  Determination of compressive strength of concrete using Self Organization Feature Map (SOFM) , 2013, Engineering with Computers.

[27]  Wen-der Yu,et al.  An integrated proactive knowledge management model for enhancing engineering services , 2012 .

[28]  Pierre-Claude Aitcin,et al.  Cements of yesterday and today Concrete of tomorrow , 2000 .

[29]  Shu-Hsien Liao,et al.  System perspective of knowledge management, organizational learning, and organizational innovation , 2010, Expert Syst. Appl..

[30]  Mohsen Hamedi,et al.  Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm , 2014, Journal of Intelligent Manufacturing.

[31]  Mahmut Bilgehan,et al.  A comparative study for the concrete compressive strength estimation using neural network and neuro-fuzzy modelling approaches , 2011 .

[32]  Ching-Hsue Cheng,et al.  Classification knowledge discovery in mold tooling test using decision tree algorithm , 2011, J. Intell. Manuf..

[33]  Hui-Ling Huang,et al.  Knowledge management fit and its implications for business performance: A profile deviation analysis , 2012, Knowl. Based Syst..

[34]  Selim Zaim,et al.  Universal structure modeling approach to customer satisfaction index , 2013, Ind. Manag. Data Syst..

[35]  David C. Yen,et al.  Exploring barriers to knowledge flow at different knowledge management maturity stages , 2012, Inf. Manag..

[36]  Jieh-Haur Chen,et al.  Assessing impacts of information technology on project success through knowledge management practice , 2012 .

[37]  Cameron Switzer,et al.  Time for change: empowering organizations to succeed in the knowledge economy , 2008, J. Knowl. Manag..

[38]  Nuria Forcada,et al.  Knowledge management perceptions in construction and design companies , 2013 .

[39]  Oscar F. Bustinza,et al.  Music business models and piracy , 2013, Ind. Manag. Data Syst..

[40]  Hifjur Raheman,et al.  Predicting the draught requirement of tillage implements in sandy clay loam soil using an artificial neural network , 2009 .

[41]  Yu-Cheng Lin,et al.  Developing project communities of practice-based knowledge management system in construction , 2012 .

[42]  Ali Akbar Ramezanianpour,et al.  Hybrid support vector regression – Particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin , 2012 .

[43]  Ali Azadeh,et al.  An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units , 2011, J. Intell. Manuf..

[44]  Manoj Kumar Tiwari,et al.  Data mining in manufacturing: a review based on the kind of knowledge , 2009, J. Intell. Manuf..

[45]  I-Cheng Yeh,et al.  Knowledge discovery of concrete material using Genetic Operation Trees , 2009, Expert Syst. Appl..

[46]  Janis Grundspenkis,et al.  Agent based approach for organization and personal knowledge modelling: knowledge management perspective , 2007, J. Intell. Manuf..

[47]  Harun Tanyildizi,et al.  Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network , 2011 .

[48]  Chad Syverson,et al.  Markets: Ready-Mixed Concrete , 2008 .

[49]  Keng-Boon Ooi,et al.  Productivity management: integrating the intellectual capital , 2013, Ind. Manag. Data Syst..

[50]  P. Bowen,et al.  Changes in portlandite morphology with solvent composition: Atomistic simulations and experiment , 2011 .

[51]  David Newton Richardson,et al.  Concrete Production Plant Variables Affecting Flexural Strength Relative to Compressive Strength , 2014 .