Papers

@article{Rojo-López2024,

title = {Genetic programming to understand the influence of new sustainable powder materials in the fresh performance of cement pastes},

author = {Gemma Rojo-López and Belén González-Fonteboa and Juan Luis Pérez-Ordóñez and Fernando Martínez-Abella},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189693470&doi=10.1016%2fj.jobe.2024.109186&partnerID=40&md5=82b0d972a8049ef5dccd64581bae8dd4},

doi = {10.1016/j.jobe.2024.109186},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Journal of Building Engineering},

volume = {88},

abstract = {This study focused on pastes that incorporate metakaolin, biomass ash, and granite powder as supplementary cementitious materials to obtain specific expressions to predict rheological properties in pastes and to define the most appropriate dosage parameters using genetic programming. For this purpose, a dataset was developed following a central composite design, and some fresh properties were measured: Marsh cone and rheological properties, such as yield stress and plastic viscosity. The models generated by genetic programming presented robust statistical indices for the properties studied. The influence of supplementary cementitious materials on rheological properties was also analysed through a parametric analysis. After analysing the factors affecting paste rheology, it was concluded that the most important aspects affecting fresh behaviour were water demand and particle interaction, as well as the relation between both effects. © 2024 The Author(s)},

note = {Cited by: 0; All Open Access, Green Open Access, Hybrid Gold Open Access},

keywords = {Biomass ashes, Cement paste, Cements, Fresh performance, Genetic algorithms, Genetic programming, Granite, Granite powder, Metakaolins, Parametric analysis, Performance of cement, Powder material, Rheological property, Rheology, Supplementary cementitious material, Yield stress},

pubstate = {published},

tppubtype = {article}

}

@article{rojo-lopez_parametric_2023,

title = {Parametric analysis in sustainable self-compacting mortars using genetic programming},

author = {Gemma Rojo-López and Belén González-Fonteboa and Juan Luis Pérez-Ordóñez and Fernando Martínez-Abella},

url = {https://www.sciencedirect.com/science/article/pii/S0950061823029069},

doi = {10.1016/j.conbuildmat.2023.133189},

issn = {0950-0618},

year  = {2023},

date = {2023-11-01},

urldate = {2023-11-01},

journal = {Construction and Building Materials},

volume = {404},

pages = {133189},

abstract = {This study addresses the capabilities of genetic programming to predict the behaviour of cement-based mixtures by focusing on the influence of quaternary binders (incorporating metakaolin, biomass ash and granite powder as novel powder materials) on the rheological properties of self-compacting mixtures, including spread diameter in mini-cone test and time to flow in mini-funnel test. Using a previous dataset, GP techniques are applied to obtain predicted models and to compare them with those developed throughout analysis of variance. The results demonstrate that the equations obtained by the GP technique showed the best statistical indices for the analysed properties. Afterwards, a parametric analysis was performed to analyse the influence of the composition of quaternary binders on the fresh behaviour of mortar mixtures. The parametric analysis indicated that changes in the binder composition that increased granite powder content damage the fresh behaviour of the mortars (the funnel time increases, and the spread diameter decreases), being this negative effect more significant when the water content is low, and especially noteworthy in the Tfunnel time. It is concluded that genetic programming and design of experiments are powerful tools that can be used to analyse the influence of new raw materials in different mortar properties.},

keywords = {Biomass ash, Genetic programming, Granite powder, Supplementary cementitious materials},

pubstate = {published},

tppubtype = {article}

}

@article{Castro2015310,

title = {Genetic programming and floating boom performance},

author = {Alberte Castro and Juan Luis Pérez-Ordóñez and Juan R. Rabuñal-Dopico and G. Iglesias},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84933522330&doi=10.1016%2fj.oceaneng.2015.05.023&partnerID=40&md5=d597daf8e08b7ba3bd2af1276bba03d0},

doi = {10.1016/j.oceaneng.2015.05.023},

issn = {00298018},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Ocean Engineering},

volume = {104},

pages = {310-318},

publisher = {Elsevier Ltd},

abstract = {In this paper the performance of floating booms under waves and currents is investigated by means of genetic programming (GP). This artificial intelligence (AI) technique is used to establish a mathematical expression of the significant effective draft, an essential parameter in predicting the containment capability of floating booms, and more specifically the occurrence of drainage failure. Obtained by applying GP to a comprehensive dataset of wave-current flume experiments, the expression makes the relationships among the relevant variables explicit - an advantage relative to other AI techniques such as artificial neural networks (ANN). The expression was selected as the most adequate to represent this physical problem from various expressions generated in two different stages in which dimensional and dimensionless variables were considered as input and output variables respectively. The most representative expressions obtained in both stages are presented and compared taking into account their goodness-of-fit, physical meaning, coherence and complexity. In addition, the adjustment with the experimental data obtained with these expressions is also discussed and compared with a previously developed ANN model. © 2015 Elsevier Ltd.All rights reserved.},

note = {cited By 15},

keywords = {artificial intelligence; artificial neural network; drainage; failure analysis; floating boom; genetic algorithm; performance assessment, Artificial intelligence; Complex networks; Genetic algorithms; Neural networks, Dimensionless variables; Drainage failure; Effective draft; Floating booms; Input and outputs; Mathematical expressions; Physical model; Waves and currents, Genetic programming},

pubstate = {published},

tppubtype = {article}

}

@article{Cladera2014479,

title = {Shear strength of RC beams. Precision, accuracy, safety and simplicity using genetic programming},

author = {Antoni Cladera and Juan Luis Pérez-Ordóñez and Fernando Martínez-Abella},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908464057&doi=10.12989%2fcac.2014.14.4.479&partnerID=40&md5=3594ceb3b0c68e18b1bd30d177d85270},

doi = {10.12989/cac.2014.14.4.479},

issn = {15988198},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Computers and Concrete},

volume = {14},

number = {4},

pages = {479-501},

publisher = {Techno-Press},

abstract = {This paper presents the improvement of the EC-2 and EHE-08 shear strength formulations for concrete beams with shear reinforcement. The employed method is based on the genetic programming (GP) technique, which is configured to generate symbolic regression from a set of experimental data by considering the interactions among precision, accuracy, safety and simplicity. The size effect and the influence of the amount of shear reinforcement are examined. To develop and verify the models, 257 experimental tests on concrete beams from the literature are used. Three expressions of considerable simplicity, which significantly improve the shear strength prediction with respect to the formulations of the different studied codes, are proposed. Copyright © 2014 Techno-Press, Ltd.},

note = {cited By 21},

keywords = {Axial forces; Concrete beam; Continuity detail; Experimental test; Shear reinforcement; Shear strength predictions; Size effects; Symbolic regression, Concrete beams and girders; Diaphragms; Finite element method; Genetic algorithms; Reinforcement; Shear flow, Genetic programming},

pubstate = {published},

tppubtype = {article}

}

@article{Rabuñal2011223,

title = {Genetic programming for prediction of water flow and transport of solids in a basin},

author = {Juan R. Rabuñal-Dopico and Jerónimo Puertas-Agudo and Daniel Rivero and Ignacio Fraga and Luis Cea-Gómez and Marta Garrido},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79956323502&doi=10.1007%2f978-3-642-21326-7_25&partnerID=40&md5=d0623ece3519d4ad3c0073aae5435b26},

doi = {10.1007/978-3-642-21326-7_25},

issn = {03029743},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {6687 LNCS},

number = {PART 2},

pages = {223-232},

abstract = {One of the applications of Data Mining is the extraction of knowledge from time series [1][2]. The Evolutionary Computation (EC) and the Artificial Neural Networks (ANNs) have proved to be suitable in Data Mining for handling this type of series [3] [4]. This paper presents the use of Genetic Programming (GP) for the prediction of time series in the field of Civil Engineering where the predictive structure does not follow the classic paradigms. In this specific case, the GP technique is applied to two phenomenon that models the process where, for a specific area, the fallen rain concentrates and flows on the surface, and later from the water flows is predicted the solids transport. In this article it is shown the Genetic Programming technique use for the water flows and the solids transport prediction. It is achieved good results both in the water flow prediction as in the solids transport prediction. © 2011 Springer-Verlag Berlin Heidelberg.},

note = {cited By 1},

keywords = {Artificial neural networks; Evolutionary computations; Genetic programming technique; Predictive structures; Solids transport; Specific areas; Water flows, Civil engineering; Data handling; Data mining; Evolutionary algorithms; Flow of water; Forecasting; Hydraulics; Neural networks; Time series, Genetic programming},

pubstate = {published},

tppubtype = {article}

}

@article{Pérez2008452,

title = {Applying genetic programming to civil engineering in the improvement of models, codes and norms},

author = {Juan Luis Pérez-Ordóñez and Mónica Miguélez and Juan R. Rabuñal-Dopico and Fernando Martínez-Abella},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70449717049&doi=10.1007%2f978-3-540-88309-8_46&partnerID=40&md5=712396029eac63a87210e929fd0c05cf},

doi = {10.1007/978-3-540-88309-8_46},

issn = {03029743},

year  = {2008},

date = {2008-01-01},

urldate = {2008-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {5290 LNAI},

pages = {452-460},

abstract = {This paper presents the use of Evolutionary Computation (EC) techniques, and more specifically the Genetic Programming (GP) technique, to Civil Engineering. This technique is applied here to a phenomenon that models the performance of structural concrete under controlled conditions throughout time. Several modifications were applied to the classic GP algorithm given the temporal nature of the case to be studied; one of these modifications was the incorporation of a new operator for providing the temporal ability for this specific case. The fitness function has been also modified by adding a security coefficient that adjusts the GP-obtained expressions and penalises the expressions that return unstable values. © 2008 Springer-Verlag.},

note = {cited By 7},

keywords = {Artificial intelligence; Calculations; Civil engineering; Creep; Function evaluation; Genetic algorithms; Information analysis; Mathematical operators; Models, Concrete creep; Controlled conditions; Evolutionary computation; Evolutionary computations; Fitness functions; GP algorithm; Information Extraction; Security coefficient; Structural concretes, Genetic programming},

pubstate = {published},

tppubtype = {article}

}

@article{Rabuñal2003606,

title = {Rules and generalization capacity extraction from ANN with GP},

author = {Juan R. Rabuñal-Dopico and Julián Dorado and Alejandro Pazos and Daniel Rivero},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84892514004&doi=10.1007%2f3-540-44868-3_77&partnerID=40&md5=20e6d89e19aa827cef94bad605778307},

doi = {10.1007/3-540-44868-3_77},

issn = {03029743},

year  = {2003},

date = {2003-01-01},

urldate = {2003-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {2686},

pages = {606-613},

publisher = {Springer Verlag},

abstract = {Different techniques for extracting Artificial Neural Networks (ANN) rules have been used up to the present time, but most of them have focused on certain types of networks and their training. However, there are practically no methods which deal with ANN rule-discovery as systems that are independent from their architecture, training, and internal distribution of weights, connections, and activation functions. This paper proposes a method based on Genetic Programming (GP) with the purpose of achieving the generalization capacity characteristic of ANNs, by means of symbolic rules which can be understood by human beings. © Springer-Verlag Berlin Heidelberg 2003.},

note = {cited By 4},

keywords = {Activation functions; Generalization capacity; Human being; Rule discovery, Genetic algorithms; Neural networks, Genetic programming},

pubstate = {published},

tppubtype = {article}

}

@article{Dorado2002485,

title = {Automatic recurrent and feed-forward ANN rule and expression extraction with genetic programming},

author = {Julián Dorado and Juan R. Rabuñal-Dopico and Antonino Santos and Alejandro Pazos and Daniel Rivero},

editor = {Beyer H. -G. Adamidis P. Guervos J.J.M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84944323399&doi=10.1007%2f3-540-45712-7_47&partnerID=40&md5=6f8705235a670ee033f772d4be4cefc9},

doi = {10.1007/3-540-45712-7_47},

issn = {03029743},

year  = {2002},

date = {2002-01-01},

urldate = {2002-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {2439},

pages = {485-494},

publisher = {Springer Verlag},

abstract = {Various rule-extraction techniques using ANN have been used so far, most of them being applied on multi-layer ANN, since they are more easily handled. In many cases, extraction methods focusing on different types of networks and training have been implemented. However, there are virtually no methods that view the extraction of rules from ANN as systems which are independent from their architecture, training and internal distribution of weights, connections and activation functions. This paper proposes a ruleextraction system of ANN regardless of their architecture (multi-layer or recurrent), using Genetic Programming as a rule-exploration technique. © Springer-Verlag Berlin Heidelberg 2002.},

note = {cited By 4},

keywords = {Activation functions; Exploration techniques; Extraction method; Feed forward; Rule extraction, Extraction; Genetic algorithms; Network architecture, Genetic programming},

pubstate = {published},

tppubtype = {article}

}

@article{Dorado2002190,

title = {Prediction and modelling of the flow of a typical urban basin through genetic programming},

author = {Julián Dorado and Juan R. Rabuñal-Dopico and Jerónimo Puertas-Agudo and Antonino Santos and Daniel Rivero},

editor = {Cagnoni S.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-44349112109&doi=10.1007%2f3-540-46004-7_20&partnerID=40&md5=1b562aeac3402bc60dc76a49a52901e6},

doi = {10.1007/3-540-46004-7_20},

issn = {03029743},

year  = {2002},

date = {2002-01-01},

urldate = {2002-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {2279 LNCS},

pages = {190-201},

publisher = {Springer Verlag},

abstract = {Genetic Programming (GP) is an evolutionary method that creates computer programs that represent approximate or exact solutions to a problem. This paper proposes an application of GP in hydrology, namely for modelling the effect of rain on the run-off flow in a typical urban basin. The ultimate goal of this research is to design a real time alarm system to warn of floods or subsidence in various types of urban basin. Results look promising and appear to offer some improvement over stochastic methods for analysing river basin systems such as unitary radiographs. © Springer-Verlag Berlin Heidelberg 2002.},

note = {cited By 14},

keywords = {Alarm systems; Combinatorial optimization; Genetic algorithms; Signal processing; Stochastic systems; Computer programming, Evolutionary method; Exact solution; Real-time alarm; River basins; Stochastic methods; Typical urban, Genetic programming},

pubstate = {published},

tppubtype = {article}

}