PSI - Issue 17

Maria Apostolopoulou et al. / Procedia Structural Integrity 17 (2019) 914–923 Maria Apostolopoulou et al. / Structural Integrity Procedia 00 (2019) 000 – 000

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Apostolopoulou, M., Aggelakopoulou, E., Siouta, L., Bakolas, A., Douvika, M., Asteris, P. G., & Moropoulou, A. (2017). A methodological approach for the selection of compatible and performable restoration mortars in seismic hazard areas. Construction and Building Materials, 155, 1 - 14. Apostolopoulou, M., Delegou, E. T., Alexakis, E., Kalofonou, M., Lampropoulos, K. C., Aggelakopoulou, E., ... & Moropoulou, A. (2018). Study of the historical mortars of the Holy Aedicule as a basis for the design, application and assessment of repair mortars: A multispectral approach applied on the Holy Aedicule. Construction and Building Materials, 181, 618 - 637. Arizzi, A., Martinez - Huerga, G., Sebastián Pardo, E., & Cultrone, G. (2015). Mineralogical, textural and physical mechanical study of hydraulic lime mortars cured under different moisture conditions. Armaghani, D.J., Mohamad, E.T., Narayanasamy, M.S., Narita, N., Yagiz, S. (2017). Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunnelling and Underground Space Technology, 63, 29 - 43. Armaghani, D.J., Hajihassani, M., Mohamad, E.T., Marto, A., Noorani, S.A. (2014). Blasting - induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arabian Journal of Geosciences, 7(12), 5383 - 5396. Asteris, P.G., Tsaris, A.K., Cavaleri, L., Repapis, C.C., Papalou, A., Di Trapani, F., Karypidis, D.F. (2016). Prediction of the fundamental period of infilled RC frame structures using artificial neural networks, Computational Intelligence and Neuroscience, 2016, 5104907. Asteris, P.G., Roussis, P.C., Douvika, M.G. (2017). Feed - forward neural network prediction of the mechanical properties of sandcrete materials, Sensors (Switzerland), 17(6), 1344. Asteris, P.G., Plevris, V. (2017). Anisotropic masonry failure criterion using artificial neural networks. Neural Comput. Appl. 28, 2207–2229. Asteris, P.G., Kolovos, K.G., Douvika, M.G., Roinos, K. (2016). Prediction of self - compacting concrete strength using artificial neural networks, European Journal of Environmental and Civil Engineering, 20, pp. s102 - s122. Asteris, P.G., Nozhati, S., Nikoo, M., Cavaleri, L., Nikoo, M. (2018). Krill herd algorithm - based neural network in structural seismic reliability evaluation, Mech. Adv. Mater. Struct., doi:10.1080/15376494.2018.1430874. Asteris, P.G., Nikoo, M. (2019). Artificial Bee Colony - Based Neural Network for the Prediction of the Fundamental Period of Infilled Frame Structures, Neural Computing and Applications, DOI: 10.1007/s00521 - 018 - 03965 - 1, (Article in Press). Asteris, P.G., Kolovos, K.G. (2019). Self - compacting concrete strength prediction using surrogate models, Neural Computing and Applications, 31, 409 - 424. Barr, S., McCarter, W. J., & Suryanto, B. (2015). Bond - strength performance of hydraulic lime and natural cement mortared sandstone masonry. Construction and Building Materials, 84, 128 - 135. Bilim, C., Atiş, C. D. , Tanyildizi, H., & Karahan, O. (2009). Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network. Advances in Engineering Software, 40(5), 334 - 340. Cavaleri, L., Chatzarakis, G.E., Di Trapani, F.D., Douvika, M.G., Roinos, K., Vaxevanidis, N.M., Asteris, P.G. {2017). Modeling of surface roughness in electro - discharge machining using artificial neural networks, Advances in Materials Research (South Korea). 6(2), pp. 169 - 184. CEN. EN 459 - 1:2010. Building lime. Part 1: Definitions, specifications and conformity criteria. Brussels; 2010. Chen, H., Asteris, P.G., Armaghani, D.J., Gordan, B. and Pham, B.T. (2019). Assessing dynamic conditions of the retaining wall using two hybrid intelligent models, Applied Sciences, 2019, 9(6):1042 Cho, J. S., Moon, K. Y., Choi, M. K., Cho, K. H., Ahn, J. W., & Yeon, K. S. (2017). Performance improvement of local Korean natural hydraulic lime - based mortar using inorganic by - products. Korean Journal of Chemical Engineering, 34(5), 1385 - 1392. Delen, D., Sharda, R., Bessonov, M. (2006). Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks, Accid. Anal. Prev., 38, 434–444. Duan, Z. H., Kou, S. C., & Poon, C. S. (2013). Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Construction and Building Materials, 40, 1200 - 1206. Eskandari - Naddaf, H., & Kazemi, R. (2017). ANN prediction of cement mortar compressive strength, influence of cement strength class. Construction and Building Materials, 138, 1 - 11.