Relación de las Ondas P en los Macizos Rocosos mediante Tree of Science (ToS)

Este documento tiene como propósito evidenciar la evolución de las investigaciones realizadas a lo largo de los años sobre la relación entre los diferentes macizos rocosos y la propagación de ondas P. Para este análisis, se emplea la metodología Tree of Science, herramienta que permite dimensionar l...

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Autores:: Hincapié Salazar, Santiago

Tipo de recurso:

Fecha de publicación:: 2025

Institución:: Universidad Libre

Repositorio:: RIU - Repositorio Institucional UniLibre

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repository_id_str
dc.title.spa.fl_str_mv	Relación de las Ondas P en los Macizos Rocosos mediante Tree of Science (ToS)
dc.title.alternative.spa.fl_str_mv	Relationship of P Waves in Rock Massifs through Tree of Science (ToS)
title	Relación de las Ondas P en los Macizos Rocosos mediante Tree of Science (ToS)
spellingShingle	Relación de las Ondas P en los Macizos Rocosos mediante Tree of Science (ToS) Geomecánica Tree of Science Metodología Propiedades Geotécnicas Redes Neuronales Artificiales Tree of Science Artificial Neural networks Geomechanics Geotechnical Properties Methodology
title_short	Relación de las Ondas P en los Macizos Rocosos mediante Tree of Science (ToS)
title_full	Relación de las Ondas P en los Macizos Rocosos mediante Tree of Science (ToS)
title_fullStr	Relación de las Ondas P en los Macizos Rocosos mediante Tree of Science (ToS)
title_full_unstemmed	Relación de las Ondas P en los Macizos Rocosos mediante Tree of Science (ToS)
title_sort	Relación de las Ondas P en los Macizos Rocosos mediante Tree of Science (ToS)
dc.creator.fl_str_mv	Hincapié Salazar, Santiago
dc.contributor.advisor.none.fl_str_mv	Alzate Buitrago, Alejandro Amariles López, Cristhian Camilo
dc.contributor.author.none.fl_str_mv	Hincapié Salazar, Santiago
dc.subject.spa.fl_str_mv	Geomecánica Tree of Science Metodología Propiedades Geotécnicas Redes Neuronales Artificiales
topic	Geomecánica Tree of Science Metodología Propiedades Geotécnicas Redes Neuronales Artificiales Tree of Science Artificial Neural networks Geomechanics Geotechnical Properties Methodology
dc.subject.subjectenglish.spa.fl_str_mv	Tree of Science Artificial Neural networks Geomechanics Geotechnical Properties Methodology
description	Este documento tiene como propósito evidenciar la evolución de las investigaciones realizadas a lo largo de los años sobre la relación entre los diferentes macizos rocosos y la propagación de ondas P. Para este análisis, se emplea la metodología Tree of Science, herramienta que permite dimensionar la evolución científica del tema a través del tiempo, destacando y diferenciando los artículos y autores considerados seminales, estructurales y emergentes. Esta metodología facilita una visión integral y estructurada del desarrollo académico en torno al objeto de estudio. En este caso, el análisis aborda las propiedades geotécnicas y mecánicas de las rocas, integrando tanto aspectos teóricos como aplicados. Se profundiza en el estudio de los medios elásticos, con énfasis en la elasticidad diferencial en medios isotrópicos y porosos. La propagación de las ondas P y su interacción con las masas rocosas ha demostrado ser un tema de importancia en la ingeniería civil y la minería, ya que proporciona una comprensión sólida sobre la integridad estructural de las formaciones geológicas. Las investigaciones han abarcado desde la propagación de las ondas en distintos tipos de rocas hasta la influencia de factores como la compresión, las juntas y las fracturas en su comportamiento. El uso de herramientas matemáticas, como el análisis de Fourier, y de modelos avanzados, como el Adaptive Neuro-Fuzzy Inference System (ANFIS), ha mejorado la capacidad para predecir con mayor precisión la resistencia de las rocas, optimizando los procesos de diseño y ejecución de proyectos de construcción y extracción. Además, la aplicación de técnicas de inteligencia artificial ha revolucionado el análisis de las ondas P, permitiendo una evaluación más precisa y dinámica de la estabilidad de las estructuras rocosas. Estudios prácticos han resaltado la relevancia de comprender cómo las actividades mineras y los fenómenos naturales afectan la propagación de las ondas P. Estos hallazgos han derivado en mejoras significativas en las medidas de seguridad en las operaciones mineras y en la evaluación de riesgos asociados. En el ámbito de la ingeniería de túneles, las investigaciones han permitido entender mejor cómo las ondas influyen en la estabilidad de las estructuras subterráneas y cómo las rocas responden ante eventos extremos, como incendios o explosiones. Por otro lado, en proyectos específicos se han evaluado propiedades geotécnicas y se han propuesto ecuaciones empíricas para predecir el módulo de Young a partir de parámetros fácilmente medibles. Esto ha contribuido al desarrollo de enfoques innovadores para enfrentar los desafíos en la evaluación de las propiedades de las rocas en diversos contextos geológicos e ingenieriles. La aplicación de la metodología Tree of Science en este campo ha permitido identificar los trabajos seminales que sentaron las bases del conocimiento actual, los estudios estructurales que consolidaron teorías y métodos, y las investigaciones emergentes que proponen enfoques disruptivos e innovadores. En conjunto, estos hallazgos ofrecen una perspectiva integral sobre las complejidades geotécnicas en la ingeniería de rocas, aportando conocimientos valiosos para profesionales en el área y contribuyendo significativamente al avance del campo.
publishDate	2025
dc.date.accessioned.none.fl_str_mv	2025-01-29T15:07:17Z
dc.date.available.none.fl_str_mv	2025-01-29T15:07:17Z
dc.date.created.none.fl_str_mv	2025-01-08
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_7a1f
dc.type.local.spa.fl_str_mv	Tesis de Pregrado
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dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/10901/30514
url	https://hdl.handle.net/10901/30514
dc.relation.references.spa.fl_str_mv	Aalizad, S. A., & Rashidinejad, F. (2012). Prediction of penetration rate of rotary-percussive drilling using artificial neural networks–a case study/Prognozowanie postępu wiercenia przy użyciu wiertła udarowo-obrotowego przy wykorzystaniu sztucznych sieci neuronowych–studium przypadku. Archives of Mining Sciences. Agliardi, F., Sapigni, M., & Crosta, G. B. (2016). Rock mass characterization by high-resolution sonic and GSI borehole logging. Rock Mechanics and Rock Engineering, 49, 4303-4318. Aladejare, A. E., Alofe, E. D., Onifade, M., Lawal, A. I., Ozoji, T. M., & Zhang, Z. X. (2021). Empirical estimation of uniaxial compressive strength of rock: database of simple, multiple, and artificial intelligence-based. Ali, H. F. H. (2024). Utilizing several multivariable mathematical and M5P-tree models to predict uniaxial compressive strength of rocks. Multiscale and Multidisciplinary Modeling, Experiments and Design, 7(3), 1737-1753. Ambati, V., Sharma, S., Babu, M. N., & Nair, R. R. (2021). Laboratory measurements of ultrasonic wave velocities of rock samples and their relation to log data: A case study from Mumbai offshore. Journal of Earth System Science, 130(3), 176. Armaghani, D.J. (2016). Prediction of the uniaxialcompressive strength of sandstone using various modeling techniques. Int J Rock Mech Min, 85, 174. Avseth, P., & Carcione, J. M. (2015). Rock-physics analysis of clay-rich source rocks on the Norwegian Shelf. The Leading Edge, 34(11), 1340-1348. Bar, N., & Barton, N. (2018). Rock slope design using Q-slope and geophysical survey data. Periodica Polytechnica Civil Engineering, 62(4), 893-900. Battaglia, M., Troise, C., Obrizzo, F., Pingue, F., & De Natale, G. (2006). Evidence for fluid migration as the cause of unrest at Campi Flegrei caldera. Geophysical Research Letters, 33. Bejarbaneh, B. Y., Bejarbaneh, E. Y., Amin, M. F. M., Fahimifar, A., Jahed Armaghani, D., & Majid, M. Z. A. (2018). Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bulletin of Engineering Geology and the Environment, 77, 345-361. Birch, F. (1960). The velocity of compressional waves in rocks to 10 kilobars: 1. Journal of Geophysical Research, 65(4), 1083-1102. Chakraborty, S., Bisai, R., Roy, R., Palaniappan, S. K., Pal, S. K., & Rao, K. U. M. (2023). Predicting Young’s modulus of Indian coal measure rock using multiple regression and artificial neutral network. Journal of Sustainable Mining, 22(1). Chen, C. H., Wang, T. T., Jeng, F. S., & Huang, T. H. (2012). Mechanisms causing seismic damage of tunnels at different depths. Tunnelling and underground space technology, 28, 31-40. Ching, J., Phoon, K. K., Li, K. H., & Weng, M. C. (2019). Multivariate probability distribution for some intact rock properties. Canadian Geotechnical Journal, 56(8), 1080-1097. Ciese, R., Klose, C., & Borm, G. (2005). In situ seismic investigations of fault zones in the Leventina Gneiss Complex of the Swiss Central Alps. Geological Society, London, Special Publications, 240(1), 15-24. De Landro, G., Serlenga, V., Russo, G., Amoroso, O., Festa, G., Bruno, P. P., ... & Zollo, A. (2017). 3D ultra-high resolution seismic imaging of shallow Solfatara crater in Campi Flegrei (Italy): New insights on deep hydrothermal fluid circulation processes. Scientific Reports, 7(1), 3412. Dehghan, S., Sattari, G. H., Chelgani, S. C., & Aliabadi, M. A. (2010). Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Mining Science and Technology (China), 20(1), 41-46. Di Stefano, R., Chiarabba, C., Chiaraluce, L., Cocco, M., De Gori, P., Piccinini, D., & Valoroso, L. (2011). Fault zone properties affecting the rupture evolution of the 2009 (Mw 6.1) L'Aquila earthquake (central Italy): Insights from seismic tomography. Geophysical Research Letters, 38(10). Dong, L., Zou, W., Li, X., Shu, W., & Wang, Z. (2019). Collaborative localization method using analytical and iterative solutions for microseismic/acoustic emission sources in the rockmass structure for underground mining. Engineering Fracture Mechanics, 210, 95-112. Dou, L.M., Cai, W., Gong, S.Y., Han, R.J., & Liu, J. (2014). Dynamic Risk Assessment of Rock Burst Based on the Technology of Seismic Computed Tomography Detection. Journal of China Coal Society, 39, 238-244. Ebdali, M., Khorasani, E., & Salehin, S. (2020). A comparative study of various hybrid neural networks and regression analysis to predict unconfined compressive strength of travertine. Innovative Infrastructure Solutions, 5, 1-14. Fan, L. F., Wang, M., & Wu, Z. J. (2021). Effect of nonlinear deformational macrojoint on stress wave propagation through a double-scale discontinuous rock mass. Rock Mechanics and Rock Engineering, 54, 1077-1090. Fereidooni, D., & Khajevand, R. (2018). Determining the geotechnical characteristics of some sedimentary rocks from Iran with an emphasis on the correlations between physical, index, and mechanical properties. Geotechnical Testing Journal, 41(3), 555-573. Görgülü, K., Arpaz, E., Uysal, Ö., Durutürk, Y. S., Yüksek, A. G., Koçaslan, A., & Dilmaç, M. K. (2015). Investigation of the effects of blasting design parameters and rock properties on blast-induced ground vibrations. Arabian Journal of Geosciences, 8, 4269-4278. Heap, M. J., Baud, P., Meredith, P. G., Vinciguerra, S., & Reuschlé, T. (2014). The permeability and elastic moduli of tuff from Campi Flegrei, Italy: implications for ground deformation modelling. Solid Earth, 5(1), 25-44. He, S., Chen, T., Vennes, I., He, X., Song, D., Chen, J., & Mitri, H. (2020). Dynamic modelling of seismic wave propagation due to a remote seismic source: a case study. Rock Mechanics and Rock Engineering, 1-25. Huang, J., Zhao, M., Xu, C., Du, X., Jin, L., & Zhao, X. (2018). Seismic stability of jointed rock slopes under obliquely incident earthquake waves. Earthquake Engineering and Engineering Vibration, 17, 527-539. Jahed Armaghani, D., Safari, V., Fahimifar, A., Mohd Amin, M. F., Monjezi, M., & Mohammadi, M. A. (2018). Uniaxial compressive strength prediction through a new technique based on gene expression programming. Neural Computing and Applications, 30, 3523-3532. Jahed Armaghani, D., Tonnizam Mohamad, E., Hajihassani, M., et al. (2016). Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Engineering with Computers, 32, 189–206. Kahraman, S. A. İ. R. (2016). The prediction of penetration rate for percussive drills from indirect tests using artificial neural networks. Journal of the Southern African Institute of Mining and Metallurgy, 116(8), 793-800. Kahraman, S., Alber, M., Fener, M., & Gunaydin, O. (2010). The usability of Cerchar abrasivity index for the prediction of UCS and E of Misis Fault Breccia: regression and artificial neural networks analysis. Expert Systems with Applications, 37(12), 8750-8756. Karakus, M., & Tutmez, B. Ü. L. E. N. T. (2006). Fuzzy and multiple regression modelling for evaluation of intact rock strength based on point load, Schmidt hammer and sonic velocity. Rock mechanics and rock engineering, 39, 45-57. Khandelwal, M., li, D. J., Faradonbeh, R. S., Ranjith, P. G., & Ghoraba, S. (2016). A new model based on gene expression programming to estimate air flow in a single rock joint. Environmental Earth Sciences, 75, 1-13. Köken, E., & Kadakçı Koca, T. (2022). Evaluation of soft computing methods for estimating tangential young modulus of intact rock based on statistical performance indices. Kumar, S., Mishra, A. K., & Choudhary, B. S. (2021). Prediction of back break in blasting using random decision trees. Engineering with Computers, 1-7. Larki, E., Dehaghani, A. H. S., & Tanha, A. A. (2022). Investigation of geomechanical characteristics in one of the Iranian oilfields by using vertical seismic profile (VSP) data to predict hydraulic fracturing intervals. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 8(2), 67. Le Gonidec, Y., Schubnel, A., Wassermann, J., Gibert, D., Nussbaum, C., Kergosien, B., ... & Guéguen, Y. (2012). Field-scale acoustic investigation of a damaged anisotropic shale during a gallery excavation. International Journal of Rock Mechanics and Mining Sciences, 51, 136-148. Li, D., Armaghani, D.J., Zhou, J., et al. (2020). A GMDH Predictive Model to Predict Rock Material Strength Using Three Non-destructive Tests. J Nondestruct Eval 39, 81. Li, J., Ma, G., & Zhao, J. (2011). Analysis of Stochastic Seismic Wave Interaction with a Slippery Rock Fault. Rock Mech Rock Eng, 44, 85–92. Liang, C. Y., Zhang, Q. B., Li, X., & Xin, P. (2016). The effect of specimen shape and strain rate on uniaxial compressive behavior of rock material. Bulletin of Engineering Geology and the Environment, 75, 1669-1681. Liu, F., Ma, T., Liu, X., & Chen, F. (2019). A case study of collapses at the Yangshan tunnel of the Coal Transportation Channel from the Western Inner Mongolia to the Central China. Tunnelling and Underground Space Technology, 92, 103063. Lu, G. M., Feng, X. T., Li, Y. H., & Zhang, X. (2019). The microwave-induced fracturing of hard rock. Rock Mechanics and Rock Engineering, 52, 3017-3032. Lu, S., Zhou, C., Zhang, Z., & Jiang, N. (2019). Particle velocity response of surrounding rock of a circular tunnel subjected to cylindrical P-waves. Tunnelling and Underground Space Technology, 83, 393-400. Mahmoud, A. A., Elkatatny, S., Ali, A., & Moussa, T. (2019). Estimation of static young’s modulus for sandstone formation using artificial neural networks. Energies, 12(11), 2125. Majeed, Y., Abu Bakar, M. Z., & Butt, I. A. (2020). Abrasivity evaluation for wear prediction of button drill bits using geotechnical rock properties. Bulletin of Engineering Geology and the Environment, 79, 767-787. Majeed, Y., Emad, M. Z., Rehman, G., & Arshad, M. (2019). Block extraction of Himalayan rock salt by applying conventional dimension stone quarrying techniques. Journal of Mining Science, 55, 610-625. Martínez-Ibáñez, V., Garrido, M. E., Signes, C. H., & Tomás, R. (2021). Micro and macro-structural effects of high temperatures in Prada limestone: Key factors for future fire-intervention protocols in Tres Ponts Tunnel (Spain). Construction and Building Materials, 286, 122960. Maxwell, S. C., & Young, R. P. (1996, October). Seismic imaging of rock mass responses to excavation. In International journal of rock mechanics and mining sciences & geomechanics abstracts (Vol. 33, No. 7, pp. 713-724). Pergamon. Mohd-Nordin, M.M., Song, KI., Cho, GC., et al. (2014). Long-Wavelength Elastic Wave Propagation Across Naturally Fractured Rock Masses. Rock Mech Rock Eng, 47, 561–573. Nazarova, L. A., Zakharov, V. N., Shkuratnik, V. L., Nazarov, L. A., Protasov, M. I., & Nikolenko, P. V. (2017, June). Use of tomography in stress-strain analysis of coal-rock mass by solving boundary inverse problems. In ISRM European Rock Mechanics Symposium-EUROCK 2017. OnePetro. Neaupane, K. M., & Achet, S. H. (2004). Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya. Engineering geology, 74(3-4), 213-226. Nourani, M. H., Moghadder, M. T., & Safari, M. (2017). Classification and assessment of rock mass parameters in Choghart iron mine using P-wave velocity. Journal of Rock Mechanics and Geotechnical Engineering, 9(2), 318-328. Rahman, T., & Sarkar, K. (2021). Lithological control on the estimation of uniaxial compressive strength by the P-wave velocity using supervised and unsupervised learning. Rock Mechanics and Rock Engineering, 54, 3175-3191. Sarkar, K., Vishal, V., & Singh, T.N. (2012). An Empirical Correlation of Index Geomechanical Parameters with the Compressional Wave Velocity. Geotech Geol Eng 30, 469–479. Sharma, P. K., & Singh, T. N. (2008). A correlation between P-wave velocity, impact strength index, slake durability index and uniaxial compressive strength. Bulletin of Engineering Geology and the Environment, 67, 17-22. Siratovich, P. A., Heap, M. J., Villenueve, M. C., Cole, J. W., & Reuschlé, T. (2014). Physical property relationships of the Rotokawa Andesite, a significant geothermal reservoir rock in the Taupo Volcanic Zone, New Zealand. Geothermal Energy, 2(1), 1-31. Suleymanov, V., El-Husseiny, A., Glatz, G., & Dvorkin, J. (2023). Rock physics and machine learning comparison: elastic properties prediction and scale dependency. Frontiers in Earth Science, 11, 1095252. Sun, S., Ji, S., Wang, Q., Salisbury, M., & Kern, H. (2012). P-wave velocity differences between surface-derived and core samples from the Sulu ultrahigh-pressure terrane: Implications for in situ velocities at great depths. Geology, 40(7), 651-654. Vanorio, T., Virieux, J., Capuano, P., & Russo, G. (2005). Three‐dimensional seismic tomography from P wave and S wave microearthquake travel times and rock physics characterization of the Campi Flegrei Caldera. Journal of Geophysical Research: Solid Earth, 110(B3). Tsang, L., He, B., Rashid, A. S. A., Jalil, A. T., & Sabri, M. M. S. (2022). Predicting the Young’s modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques. Applied Sciences, 12(20), 10258. Wang, P., Pozzi, M., Small, M. J., & Harbert, W. (2015). Statistical method for early detection of changes in seismic rate associated with wastewater injections. Bulletin of the Seismological Society of America, 105(6), 2852-2862. Watanabe, T., & Sassa, K. (1995, June). Velocity and amplitude of P-waves transmitted through fractured zones composed of multiple thin low-velocity layers. In International journal of rock mechanics and mining sciences & geomechanics abstracts (Vol. 32, No. 4, pp. 313-324). Pergamon. Watanabe, T., & Sassa, K. (1996, July). Seismic attenuation tomography and its application to rock mass evaluation. In International journal of rock mechanics and mining sciences & geomechanics abstracts (Vol. 33, No. 5, pp. 467-477). Pergamon. Xu, D. P., Zhou, Y. Y., Qiu, S. L., Jiang, Q., & Wang, B. (2018). Elastic modulus deterioration index to identify the loosened zone around underground openings. Tunnelling and Underground Space Technology, 82, 20-29. Xu, Y., & Cai, M. (2017). Numerical study on the influence of cross-sectional shape on strength and deformation behaviors of rocks under uniaxial compression. Computers and Geotechnics, 84, 129-137. Yang, Y., Xu, D., Zheng, H., Wu, Z., & Huang, D. (2021). Modeling wave propagation in rock masses using the contact potential-based three-dimensional discontinuous deformation analysis method. Rock Mechanics and Rock Engineering, 54, 2465-2490. Yagiz, S. (2011). Correlation between slake durability and rock properties for some carbonate rocks. Bulletin of Engineering Geology and the Environment, 70, 377-383. Yagiz, S., Sezer, E. A., & Gokceoglu, C. (2012). Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks. International Journal for Numerical and Analytical Methods in Geomechanics, 36(14), 1636-1650. Yagiz, S., Gokceoglu, C., Sezer, E., & Iplikci, S. (2009). Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Engineering Applications of Artificial Intelligence, 22(4-5), 808-814. Yasar, E., & Erdogan, Y. (2004). Correlating sound velocity with the density, compressive strength and Young's modulus of carbonate rocks. International Journal of Rock Mechanics and Mining Sciences, 41(5), 871-875. Yi, C. P., Lu, W. B., Zhang, P., Johansson, D., & Nyberg, U. (2016). Effect of imperfect interface on the dynamic response of a circular lined tunnel impacted by plane P-waves. Tunnelling and underground space technology, 51, 68-74. Yin, T., Yang, Z., Wu, Y., Tan, X., & Li, M. (2022). Experimental investigation on the effect of open fire on the tensile properties and damage evolution behavior of granite. International Journal of Damage Mechanics, 31(8), 1139-1164. Zhang, Y., Fu, X., & Sheng, Q. (2014). Modification of the discontinuous deformation analysis method and its application to seismic response analysis of large underground caverns. Tunnelling and Underground Space Technology, 40, 241-250. Zhang, B., Zhang, G., Jiao, M., Zhang, Z., & Shu, M. (2021). Source characterization of the 2019 M2. 4 earthquakes induced by Fushun, Liaoning mining based on a dense seismic array. Chinese Journal of Geophysics, 64(4), 1227-1235. Zhao, B., Zhao, J., & Cai, J. G. (2006). P‐wave transmission across fractures with nonlinear deformational behaviour. International journal for numerical and analytical methods in geomechanics, 30(11), 1097-1112. Zhao, J., Cai, J., Zhao, X., et al. (2008). Dynamic Model of Fracture Normal Behaviour and Application to Prediction of Stress Wave Attenuation Across Fractures. Rock Mech Rock Eng, 41, 671–693. Zhu, J.B., Deng, X.F., Zhao, X.B., et al. (2013). A Numerical Study on Wave Transmission Across Multiple Intersecting Joint Sets in Rock Masses with UDEC. Rock Mech Rock Eng, 46, 1429–1442.
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spelling	Alzate Buitrago, AlejandroAmariles López, Cristhian CamiloHincapié Salazar, SantiagoPereira2025-01-29T15:07:17Z2025-01-29T15:07:17Z2025-01-08https://hdl.handle.net/10901/30514Este documento tiene como propósito evidenciar la evolución de las investigaciones realizadas a lo largo de los años sobre la relación entre los diferentes macizos rocosos y la propagación de ondas P. Para este análisis, se emplea la metodología Tree of Science, herramienta que permite dimensionar la evolución científica del tema a través del tiempo, destacando y diferenciando los artículos y autores considerados seminales, estructurales y emergentes. Esta metodología facilita una visión integral y estructurada del desarrollo académico en torno al objeto de estudio. En este caso, el análisis aborda las propiedades geotécnicas y mecánicas de las rocas, integrando tanto aspectos teóricos como aplicados. Se profundiza en el estudio de los medios elásticos, con énfasis en la elasticidad diferencial en medios isotrópicos y porosos. La propagación de las ondas P y su interacción con las masas rocosas ha demostrado ser un tema de importancia en la ingeniería civil y la minería, ya que proporciona una comprensión sólida sobre la integridad estructural de las formaciones geológicas. Las investigaciones han abarcado desde la propagación de las ondas en distintos tipos de rocas hasta la influencia de factores como la compresión, las juntas y las fracturas en su comportamiento. El uso de herramientas matemáticas, como el análisis de Fourier, y de modelos avanzados, como el Adaptive Neuro-Fuzzy Inference System (ANFIS), ha mejorado la capacidad para predecir con mayor precisión la resistencia de las rocas, optimizando los procesos de diseño y ejecución de proyectos de construcción y extracción. Además, la aplicación de técnicas de inteligencia artificial ha revolucionado el análisis de las ondas P, permitiendo una evaluación más precisa y dinámica de la estabilidad de las estructuras rocosas. Estudios prácticos han resaltado la relevancia de comprender cómo las actividades mineras y los fenómenos naturales afectan la propagación de las ondas P. Estos hallazgos han derivado en mejoras significativas en las medidas de seguridad en las operaciones mineras y en la evaluación de riesgos asociados. En el ámbito de la ingeniería de túneles, las investigaciones han permitido entender mejor cómo las ondas influyen en la estabilidad de las estructuras subterráneas y cómo las rocas responden ante eventos extremos, como incendios o explosiones. Por otro lado, en proyectos específicos se han evaluado propiedades geotécnicas y se han propuesto ecuaciones empíricas para predecir el módulo de Young a partir de parámetros fácilmente medibles. Esto ha contribuido al desarrollo de enfoques innovadores para enfrentar los desafíos en la evaluación de las propiedades de las rocas en diversos contextos geológicos e ingenieriles. La aplicación de la metodología Tree of Science en este campo ha permitido identificar los trabajos seminales que sentaron las bases del conocimiento actual, los estudios estructurales que consolidaron teorías y métodos, y las investigaciones emergentes que proponen enfoques disruptivos e innovadores. En conjunto, estos hallazgos ofrecen una perspectiva integral sobre las complejidades geotécnicas en la ingeniería de rocas, aportando conocimientos valiosos para profesionales en el área y contribuyendo significativamente al avance del campo.Universidad Libre Seccional Pereira -- Facultad de Ingeniería -- Ingeniería CivilThis document aims to highlight the evolution of research conducted over the years on the relationship between different rock masses and the propagation of P-waves. For this analysis, the Tree of Science methodology is employed—a tool that enables the scientific evolution of the subject to be assessed over time by distinguishing and categorizing articles and authors as seminal, structural, or emerging. This methodology provides a comprehensive and structured view of the academic development surrounding the topic under study. In this case, the analysis addresses the geotechnical and mechanical properties of rocks, integrating both theoretical and applied aspects. It delves into the study of elastic media, with a focus on differential elasticity in isotropic and porous media. The propagation of P-waves and their interaction with rock masses has proven to be a crucial subject in civil engineering and mining, as it provides robust insights into the structural integrity of geological formations. Research has ranged from the propagation of waves in different rock types to the influence of factors such as compression, joints, and fractures on their behavior. The use of mathematical tools such as Fourier analysis, along with advanced models like the Adaptive Neuro-Fuzzy Inference System (ANFIS), has enhanced the ability to predict rock resistance more accurately, optimizing the design and execution processes of construction and extraction projects. Furthermore, the application of artificial intelligence techniques has revolutionized the analysis of P-waves, enabling more precise and dynamic assessments of the stability of rock structures. Practical studies have emphasized the importance of understanding how mining activities and natural phenomena impact the propagation of P-waves. These findings have led to significant improvements in safety measures for mining operations and in the assessment of associated risks. In the field of tunnel engineering, research has contributed to a better understanding of how waves influence the stability of underground structures and how rocks respond to extreme events such as fires or explosions. Additionally, specific projects have evaluated geotechnical properties and proposed empirical equations to predict Young’s modulus based on easily measurable parameters. This has fostered the development of innovative approaches to address challenges in evaluating rock properties across diverse geological and engineering contexts. The application of the Tree of Science methodology in this field has enabled the identification of seminal works that laid the foundation for current knowledge, structural studies that consolidated theories and methods, and emerging research that proposes disruptive and innovative approaches. Together, these findings provide a multifaceted perspective on the geotechnical complexities in rock engineering, offering valuable insights for professionals in the field and contributing significantly to the advancement of the discipline.PDFhttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Atribución-NoComercial-SinDerivadas 2.5 Colombiainfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2GeomecánicaTree of ScienceMetodologíaPropiedades GeotécnicasRedes Neuronales ArtificialesTree of ScienceArtificial Neural networksGeomechanicsGeotechnical PropertiesMethodologyRelación de las Ondas P en los Macizos Rocosos mediante Tree of Science (ToS)Relationship of P Waves in Rock Massifs through Tree of Science (ToS)Tesis de Pregradoinfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fAalizad, S. A., & Rashidinejad, F. (2012). Prediction of penetration rate of rotary-percussive drilling using artificial neural networks–a case study/Prognozowanie postępu wiercenia przy użyciu wiertła udarowo-obrotowego przy wykorzystaniu sztucznych sieci neuronowych–studium przypadku. Archives of Mining Sciences.Agliardi, F., Sapigni, M., & Crosta, G. B. (2016). Rock mass characterization by high-resolution sonic and GSI borehole logging. Rock Mechanics and Rock Engineering, 49, 4303-4318.Aladejare, A. E., Alofe, E. D., Onifade, M., Lawal, A. I., Ozoji, T. M., & Zhang, Z. X. (2021). Empirical estimation of uniaxial compressive strength of rock: database of simple, multiple, and artificial intelligence-based.Ali, H. F. H. (2024). Utilizing several multivariable mathematical and M5P-tree models to predict uniaxial compressive strength of rocks. Multiscale and Multidisciplinary Modeling, Experiments and Design, 7(3), 1737-1753.Ambati, V., Sharma, S., Babu, M. N., & Nair, R. R. (2021). Laboratory measurements of ultrasonic wave velocities of rock samples and their relation to log data: A case study from Mumbai offshore. Journal of Earth System Science, 130(3), 176.Armaghani, D.J. (2016). Prediction of the uniaxialcompressive strength of sandstone using various modeling techniques. Int J Rock Mech Min, 85, 174.Avseth, P., & Carcione, J. M. (2015). Rock-physics analysis of clay-rich source rocks on the Norwegian Shelf. The Leading Edge, 34(11), 1340-1348.Bar, N., & Barton, N. (2018). Rock slope design using Q-slope and geophysical survey data. Periodica Polytechnica Civil Engineering, 62(4), 893-900.Battaglia, M., Troise, C., Obrizzo, F., Pingue, F., & De Natale, G. (2006). Evidence for fluid migration as the cause of unrest at Campi Flegrei caldera. Geophysical Research Letters, 33.Bejarbaneh, B. Y., Bejarbaneh, E. Y., Amin, M. F. M., Fahimifar, A., Jahed Armaghani, D., & Majid, M. Z. A. (2018). Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bulletin of Engineering Geology and the Environment, 77, 345-361.Birch, F. (1960). The velocity of compressional waves in rocks to 10 kilobars: 1. Journal of Geophysical Research, 65(4), 1083-1102.Chakraborty, S., Bisai, R., Roy, R., Palaniappan, S. K., Pal, S. K., & Rao, K. U. M. (2023). Predicting Young’s modulus of Indian coal measure rock using multiple regression and artificial neutral network. Journal of Sustainable Mining, 22(1).Chen, C. H., Wang, T. T., Jeng, F. S., & Huang, T. H. (2012). Mechanisms causing seismic damage of tunnels at different depths. Tunnelling and underground space technology, 28, 31-40.Ching, J., Phoon, K. K., Li, K. H., & Weng, M. C. (2019). Multivariate probability distribution for some intact rock properties. Canadian Geotechnical Journal, 56(8), 1080-1097.Ciese, R., Klose, C., & Borm, G. (2005). In situ seismic investigations of fault zones in the Leventina Gneiss Complex of the Swiss Central Alps. Geological Society, London, Special Publications, 240(1), 15-24.De Landro, G., Serlenga, V., Russo, G., Amoroso, O., Festa, G., Bruno, P. P., ... & Zollo, A. (2017). 3D ultra-high resolution seismic imaging of shallow Solfatara crater in Campi Flegrei (Italy): New insights on deep hydrothermal fluid circulation processes. Scientific Reports, 7(1), 3412.Dehghan, S., Sattari, G. H., Chelgani, S. C., & Aliabadi, M. A. (2010). Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Mining Science and Technology (China), 20(1), 41-46.Di Stefano, R., Chiarabba, C., Chiaraluce, L., Cocco, M., De Gori, P., Piccinini, D., & Valoroso, L. (2011). Fault zone properties affecting the rupture evolution of the 2009 (Mw 6.1) L'Aquila earthquake (central Italy): Insights from seismic tomography. Geophysical Research Letters, 38(10).Dong, L., Zou, W., Li, X., Shu, W., & Wang, Z. (2019). Collaborative localization method using analytical and iterative solutions for microseismic/acoustic emission sources in the rockmass structure for underground mining. Engineering Fracture Mechanics, 210, 95-112.Dou, L.M., Cai, W., Gong, S.Y., Han, R.J., & Liu, J. (2014). Dynamic Risk Assessment of Rock Burst Based on the Technology of Seismic Computed Tomography Detection. Journal of China Coal Society, 39, 238-244.Ebdali, M., Khorasani, E., & Salehin, S. (2020). A comparative study of various hybrid neural networks and regression analysis to predict unconfined compressive strength of travertine. Innovative Infrastructure Solutions, 5, 1-14.Fan, L. F., Wang, M., & Wu, Z. J. (2021). Effect of nonlinear deformational macrojoint on stress wave propagation through a double-scale discontinuous rock mass. Rock Mechanics and Rock Engineering, 54, 1077-1090.Fereidooni, D., & Khajevand, R. (2018). Determining the geotechnical characteristics of some sedimentary rocks from Iran with an emphasis on the correlations between physical, index, and mechanical properties. Geotechnical Testing Journal, 41(3), 555-573.Görgülü, K., Arpaz, E., Uysal, Ö., Durutürk, Y. S., Yüksek, A. G., Koçaslan, A., & Dilmaç, M. K. (2015). Investigation of the effects of blasting design parameters and rock properties on blast-induced ground vibrations. Arabian Journal of Geosciences, 8, 4269-4278.Heap, M. J., Baud, P., Meredith, P. G., Vinciguerra, S., & Reuschlé, T. (2014). The permeability and elastic moduli of tuff from Campi Flegrei, Italy: implications for ground deformation modelling. Solid Earth, 5(1), 25-44.He, S., Chen, T., Vennes, I., He, X., Song, D., Chen, J., & Mitri, H. (2020). Dynamic modelling of seismic wave propagation due to a remote seismic source: a case study. Rock Mechanics and Rock Engineering, 1-25.Huang, J., Zhao, M., Xu, C., Du, X., Jin, L., & Zhao, X. (2018). Seismic stability of jointed rock slopes under obliquely incident earthquake waves. Earthquake Engineering and Engineering Vibration, 17, 527-539.Jahed Armaghani, D., Safari, V., Fahimifar, A., Mohd Amin, M. F., Monjezi, M., & Mohammadi, M. A. (2018). Uniaxial compressive strength prediction through a new technique based on gene expression programming. Neural Computing and Applications, 30, 3523-3532.Jahed Armaghani, D., Tonnizam Mohamad, E., Hajihassani, M., et al. (2016). Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Engineering with Computers, 32, 189–206.Kahraman, S. A. İ. R. (2016). The prediction of penetration rate for percussive drills from indirect tests using artificial neural networks. Journal of the Southern African Institute of Mining and Metallurgy, 116(8), 793-800.Kahraman, S., Alber, M., Fener, M., & Gunaydin, O. (2010). The usability of Cerchar abrasivity index for the prediction of UCS and E of Misis Fault Breccia: regression and artificial neural networks analysis. Expert Systems with Applications, 37(12), 8750-8756.Karakus, M., & Tutmez, B. Ü. L. E. N. T. (2006). Fuzzy and multiple regression modelling for evaluation of intact rock strength based on point load, Schmidt hammer and sonic velocity. Rock mechanics and rock engineering, 39, 45-57.Khandelwal, M., li, D. J., Faradonbeh, R. S., Ranjith, P. G., & Ghoraba, S. (2016). A new model based on gene expression programming to estimate air flow in a single rock joint. Environmental Earth Sciences, 75, 1-13.Köken, E., & Kadakçı Koca, T. (2022). Evaluation of soft computing methods for estimating tangential young modulus of intact rock based on statistical performance indices.Kumar, S., Mishra, A. K., & Choudhary, B. S. (2021). Prediction of back break in blasting using random decision trees. Engineering with Computers, 1-7.Larki, E., Dehaghani, A. H. S., & Tanha, A. A. (2022). Investigation of geomechanical characteristics in one of the Iranian oilfields by using vertical seismic profile (VSP) data to predict hydraulic fracturing intervals. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 8(2), 67.Le Gonidec, Y., Schubnel, A., Wassermann, J., Gibert, D., Nussbaum, C., Kergosien, B., ... & Guéguen, Y. (2012). Field-scale acoustic investigation of a damaged anisotropic shale during a gallery excavation. International Journal of Rock Mechanics and Mining Sciences, 51, 136-148.Li, D., Armaghani, D.J., Zhou, J., et al. (2020). A GMDH Predictive Model to Predict Rock Material Strength Using Three Non-destructive Tests. J Nondestruct Eval 39, 81.Li, J., Ma, G., & Zhao, J. (2011). Analysis of Stochastic Seismic Wave Interaction with a Slippery Rock Fault. Rock Mech Rock Eng, 44, 85–92.Liang, C. Y., Zhang, Q. B., Li, X., & Xin, P. (2016). The effect of specimen shape and strain rate on uniaxial compressive behavior of rock material. Bulletin of Engineering Geology and the Environment, 75, 1669-1681.Liu, F., Ma, T., Liu, X., & Chen, F. (2019). A case study of collapses at the Yangshan tunnel of the Coal Transportation Channel from the Western Inner Mongolia to the Central China. Tunnelling and Underground Space Technology, 92, 103063.Lu, G. M., Feng, X. T., Li, Y. H., & Zhang, X. (2019). The microwave-induced fracturing of hard rock. Rock Mechanics and Rock Engineering, 52, 3017-3032.Lu, S., Zhou, C., Zhang, Z., & Jiang, N. (2019). Particle velocity response of surrounding rock of a circular tunnel subjected to cylindrical P-waves. Tunnelling and Underground Space Technology, 83, 393-400.Mahmoud, A. A., Elkatatny, S., Ali, A., & Moussa, T. (2019). Estimation of static young’s modulus for sandstone formation using artificial neural networks. Energies, 12(11), 2125.Majeed, Y., Abu Bakar, M. Z., & Butt, I. A. (2020). Abrasivity evaluation for wear prediction of button drill bits using geotechnical rock properties. Bulletin of Engineering Geology and the Environment, 79, 767-787.Majeed, Y., Emad, M. Z., Rehman, G., & Arshad, M. (2019). Block extraction of Himalayan rock salt by applying conventional dimension stone quarrying techniques. Journal of Mining Science, 55, 610-625.Martínez-Ibáñez, V., Garrido, M. E., Signes, C. H., & Tomás, R. (2021). Micro and macro-structural effects of high temperatures in Prada limestone: Key factors for future fire-intervention protocols in Tres Ponts Tunnel (Spain). Construction and Building Materials, 286, 122960.Maxwell, S. C., & Young, R. P. (1996, October). Seismic imaging of rock mass responses to excavation. In International journal of rock mechanics and mining sciences & geomechanics abstracts (Vol. 33, No. 7, pp. 713-724). Pergamon.Mohd-Nordin, M.M., Song, KI., Cho, GC., et al. (2014). Long-Wavelength Elastic Wave Propagation Across Naturally Fractured Rock Masses. Rock Mech Rock Eng, 47, 561–573.Nazarova, L. A., Zakharov, V. N., Shkuratnik, V. L., Nazarov, L. A., Protasov, M. I., & Nikolenko, P. V. (2017, June). Use of tomography in stress-strain analysis of coal-rock mass by solving boundary inverse problems. In ISRM European Rock Mechanics Symposium-EUROCK 2017. OnePetro.Neaupane, K. M., & Achet, S. H. (2004). Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya. Engineering geology, 74(3-4), 213-226.Nourani, M. H., Moghadder, M. T., & Safari, M. (2017). Classification and assessment of rock mass parameters in Choghart iron mine using P-wave velocity. Journal of Rock Mechanics and Geotechnical Engineering, 9(2), 318-328.Rahman, T., & Sarkar, K. (2021). Lithological control on the estimation of uniaxial compressive strength by the P-wave velocity using supervised and unsupervised learning. Rock Mechanics and Rock Engineering, 54, 3175-3191.Sarkar, K., Vishal, V., & Singh, T.N. (2012). An Empirical Correlation of Index Geomechanical Parameters with the Compressional Wave Velocity. Geotech Geol Eng 30, 469–479.Sharma, P. K., & Singh, T. N. (2008). A correlation between P-wave velocity, impact strength index, slake durability index and uniaxial compressive strength. Bulletin of Engineering Geology and the Environment, 67, 17-22.Siratovich, P. A., Heap, M. J., Villenueve, M. C., Cole, J. W., & Reuschlé, T. (2014). Physical property relationships of the Rotokawa Andesite, a significant geothermal reservoir rock in the Taupo Volcanic Zone, New Zealand. Geothermal Energy, 2(1), 1-31.Suleymanov, V., El-Husseiny, A., Glatz, G., & Dvorkin, J. (2023). Rock physics and machine learning comparison: elastic properties prediction and scale dependency. Frontiers in Earth Science, 11, 1095252.Sun, S., Ji, S., Wang, Q., Salisbury, M., & Kern, H. (2012). P-wave velocity differences between surface-derived and core samples from the Sulu ultrahigh-pressure terrane: Implications for in situ velocities at great depths. Geology, 40(7), 651-654.Vanorio, T., Virieux, J., Capuano, P., & Russo, G. (2005). Three‐dimensional seismic tomography from P wave and S wave microearthquake travel times and rock physics characterization of the Campi Flegrei Caldera. Journal of Geophysical Research: Solid Earth, 110(B3).Tsang, L., He, B., Rashid, A. S. A., Jalil, A. T., & Sabri, M. M. S. (2022). Predicting the Young’s modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques. Applied Sciences, 12(20), 10258.Wang, P., Pozzi, M., Small, M. J., & Harbert, W. (2015). Statistical method for early detection of changes in seismic rate associated with wastewater injections. Bulletin of the Seismological Society of America, 105(6), 2852-2862.Watanabe, T., & Sassa, K. (1995, June). Velocity and amplitude of P-waves transmitted through fractured zones composed of multiple thin low-velocity layers. In International journal of rock mechanics and mining sciences & geomechanics abstracts (Vol. 32, No. 4, pp. 313-324). Pergamon.Watanabe, T., & Sassa, K. (1996, July). Seismic attenuation tomography and its application to rock mass evaluation. In International journal of rock mechanics and mining sciences & geomechanics abstracts (Vol. 33, No. 5, pp. 467-477). Pergamon.Xu, D. P., Zhou, Y. Y., Qiu, S. L., Jiang, Q., & Wang, B. (2018). Elastic modulus deterioration index to identify the loosened zone around underground openings. Tunnelling and Underground Space Technology, 82, 20-29.Xu, Y., & Cai, M. (2017). Numerical study on the influence of cross-sectional shape on strength and deformation behaviors of rocks under uniaxial compression. Computers and Geotechnics, 84, 129-137.Yang, Y., Xu, D., Zheng, H., Wu, Z., & Huang, D. (2021). Modeling wave propagation in rock masses using the contact potential-based three-dimensional discontinuous deformation analysis method. Rock Mechanics and Rock Engineering, 54, 2465-2490.Yagiz, S. (2011). Correlation between slake durability and rock properties for some carbonate rocks. Bulletin of Engineering Geology and the Environment, 70, 377-383.Yagiz, S., Sezer, E. A., & Gokceoglu, C. (2012). Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks. International Journal for Numerical and Analytical Methods in Geomechanics, 36(14), 1636-1650.Yagiz, S., Gokceoglu, C., Sezer, E., & Iplikci, S. (2009). Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Engineering Applications of Artificial Intelligence, 22(4-5), 808-814.Yasar, E., & Erdogan, Y. (2004). Correlating sound velocity with the density, compressive strength and Young's modulus of carbonate rocks. International Journal of Rock Mechanics and Mining Sciences, 41(5), 871-875.Yi, C. P., Lu, W. B., Zhang, P., Johansson, D., & Nyberg, U. (2016). Effect of imperfect interface on the dynamic response of a circular lined tunnel impacted by plane P-waves. Tunnelling and underground space technology, 51, 68-74.Yin, T., Yang, Z., Wu, Y., Tan, X., & Li, M. (2022). Experimental investigation on the effect of open fire on the tensile properties and damage evolution behavior of granite. International Journal of Damage Mechanics, 31(8), 1139-1164.Zhang, Y., Fu, X., & Sheng, Q. (2014). Modification of the discontinuous deformation analysis method and its application to seismic response analysis of large underground caverns. Tunnelling and Underground Space Technology, 40, 241-250.Zhang, B., Zhang, G., Jiao, M., Zhang, Z., & Shu, M. (2021). Source characterization of the 2019 M2. 4 earthquakes induced by Fushun, Liaoning mining based on a dense seismic array. Chinese Journal of Geophysics, 64(4), 1227-1235.Zhao, B., Zhao, J., & Cai, J. G. (2006). P‐wave transmission across fractures with nonlinear deformational behaviour. International journal for numerical and analytical methods in geomechanics, 30(11), 1097-1112.Zhao, J., Cai, J., Zhao, X., et al. (2008). Dynamic Model of Fracture Normal Behaviour and Application to Prediction of Stress Wave Attenuation Across Fractures. Rock Mech Rock Eng, 41, 671–693.Zhu, J.B., Deng, X.F., Zhao, X.B., et al. (2013). A Numerical Study on Wave Transmission Across Multiple Intersecting Joint Sets in Rock Masses with UDEC. 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Relación de las Ondas P en los Macizos Rocosos mediante Tree of Science (ToS)

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