Papers

@article{rojo-lopez_genetic_2024,

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.sciencedirect.com/science/article/pii/S235271022400754X},

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

issn = {2352-7102},

year  = {2024},

date = {2024-07-01},

urldate = {2024-07-01},

journal = {Journal of Building Engineering},

volume = {88},

pages = {109186},

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.},

keywords = {Artificial intelligence, Biomass ash, Granite powder, Metakaolin, Parametric analysis},

pubstate = {published},

tppubtype = {article}

}

@article{ramos_predicting_2023,

title = {Predicting Critical Speed of Railway Tracks Using Artificial Intelligence Algorithms},

author = {Ana Ramos and Alexandre Castanheira-Pinto and Aires Colaço and Jesús Fernández-Ruíz and Pedro Alves Costa},

url = {https://www.mdpi.com/2571-631X/6/4/53},

doi = {10.3390/vibration6040053},

issn = {2571-631X},

year  = {2023},

date = {2023-12-01},

urldate = {2026-08-13},

journal = {Vibration},

volume = {6},

number = {4},

pages = {895–916},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {Motivated by concerns regarding safety and maintenance, the operational speed of a railway line must remain significantly below the critical speed associated with the track–ground system. Given the large number of track sections within a railway corridor that potentially need to be analyzed, the development of efficient predictive tools is of the utmost importance. Based on that, the problem can be analyzed in a few seconds instead of taking several hours of computational effort, as required by a numerical analysis. In this context, and for the first time, machine learning algorithms, namely artificial neural networks and support vector machine techniques, are applied to this particular issue. For its derivation, a reliable and robust dataset was developed by means of advanced numerical methodologies that were previously experimentally validated. The database is available as supplemental data and may be used by other researchers. Regarding the prediction process, the performance of both models was very satisfactory. From the results achieved, it is possible to conclude that the prediction tool is a novel and reliable approach for an almost instantaneous prediction of critical speed in a high number of track sections.},

keywords = {Artificial intelligence, critical speed, numerical modeling, Railway dynamics, scoping tool},

pubstate = {published},

tppubtype = {article}

}

@article{bienvenido-huertas_automation_2020,

title = {Automation and optimization of in-situ assessment of wall thermal transmittance using a Random Forest algorithm},

author = {David Bienvenido-Huertas and Carlos Rubio-Bellido and Juan Luis Pérez-Ordóñez and Miguel José Oliveira},

url = {https://www.sciencedirect.com/science/article/pii/S0360132319306912},

doi = {10.1016/j.buildenv.2019.106479},

issn = {0360-1323},

year  = {2020},

date = {2020-01-01},

urldate = {2026-08-06},

journal = {Building and Environment},

volume = {168},

pages = {106479},

abstract = {Reducing energy consumption and greenhouse gases emissions is among the main challenges of building sector. It is therefore crucial to know the characteristics of envelopes. There are experimental methods to determine thermal transmittance, but limitations are presented. By using techniques of artificial intelligence, this article solves the limitations of current methods by predicting correctly the thermal transmittance value of ISO 6946 and the building period of a wall with monitored data. The methodology used is extrapolated to any country: 163 real monitorings and 140 different typologies of walls have been combined to generate the dataset (22,820 items). The results show the optimal operation of the Random Forest algorithm because both the thermal transmittance of ISO 6946 and the building period are determined by using the most common methods: the heat flow meter method and the thermometric method. This study makes progress towards more automatized processes to characterize thermal transmittance.},

keywords = {Artificial intelligence, Building period, In-situ, ISO 6946, Random forests, Thermal transmittance},

pubstate = {published},

tppubtype = {article}

}

@article{Dorado2003329,

title = {Prediction and modeling of the rainfall-runoff transformation of a typical urban basin using ann and gp},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0242415241&doi=10.1080%2f713827142&partnerID=40&md5=6ef91bd35fce5eeb4013706374efb950},

doi = {10.1080/713827142},

issn = {08839514},

year  = {2003},

date = {2003-01-01},

urldate = {2003-01-01},

journal = {Applied Artificial Intelligence},

volume = {17},

number = {4},

pages = {329-343},

abstract = {This paper proposes an application of Genetic Programming (GP) and Artificial Neural Networks (ANN) in hydrology, showing how these two techniques can work together to solve a problem, namely for modeling the effect of rain on the runoff 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 basins. Results look promising and appear to offer some improvement for analyzing river basin systems over stochastic methods such as unitary hydrographs. © 2003 Taylor and Francis Group, LLC.},

note = {cited By 75},

keywords = {Artificial intelligence, Genetic programming; Runoff flow; Stochastic methods, Hydrology; Mathematical models; Mathematical programming; Neural networks; Rain; Runoff; Statistical methods},

pubstate = {published},

tppubtype = {article}

}