Panagiotis G. Asteris mostly deals with Structural engineering, Artificial neural network, Masonry, Infill and Compressive strength. His research integrates issues of Polynomial and Tensor in his study of Structural engineering. His studies deal with areas such as Tree, Particle swarm optimization and Gene expression programming as well as Artificial neural network.
The concepts of his Masonry study are interwoven with issues in Representation, Structural system and Finite element method. His Infill research integrates issues from Frame, Geotechnical engineering and Stiffness. His Compressive strength study combines topics from a wide range of disciplines, such as Ultimate tensile strength, Normalization, Swarm behaviour and Relation.
His scientific interests lie mostly in Structural engineering, Masonry, Artificial neural network, Geotechnical engineering and Compressive strength. His research brings together the fields of Frame and Structural engineering. His Masonry research incorporates themes from Structural system, Diagonal and Earthquake resistant.
His work on Soft computing as part of general Artificial neural network research is often related to Anisotropy and Strengths based, thus linking different fields of science. His Geotechnical engineering study which covers Unreinforced masonry building that intersects with Orthotropic material. His work on Metakaolin as part of general Compressive strength study is frequently linked to Experimental data, therefore connecting diverse disciplines of science.
Panagiotis G. Asteris spends much of his time researching Artificial neural network, Structural engineering, Compressive strength, Artificial intelligence and Mortar. His Artificial neural network research is multidisciplinary, relying on both Rock mass classification, Swarm behaviour, Ensemble forecasting, Deflection and Robustness. When carried out as part of a general Structural engineering research project, his work on Reinforced concrete, Infill and Stiffness is frequently linked to work in Adaptive neuro fuzzy inference system, therefore connecting diverse disciplines of study.
His Compressive strength research includes elements of Normalization, Geotechnical engineering, Aggregate and Cement. His work investigates the relationship between Artificial intelligence and topics such as Machine learning that intersect with problems in Chinese city. His Cement mortar study, which is part of a larger body of work in Mortar, is frequently linked to Mix design, bridging the gap between disciplines.
Panagiotis G. Asteris mainly investigates Artificial neural network, Structural engineering, Compressive strength, Soft computing and Ultimate tensile strength. His Artificial neural network research is multidisciplinary, incorporating perspectives in Construction engineering, Seismic assessment, Normalization and Masonry. His work in the fields of Reinforced concrete, Retaining wall and Infill overlaps with other areas such as Sampling.
In his research on the topic of Compressive strength, Metakaolin and Cement is strongly related with Mortar. His Soft computing study incorporates themes from Shear strength and Transverse reinforcement. His Ultimate tensile strength study integrates concerns from other disciplines, such as Gene expression programming, Bearing capacity, Swarm behaviour and Particle swarm optimization.
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Lateral Stiffness of Brick Masonry Infilled Plane Frames
P. G. Asteris.
Journal of Structural Engineering-asce (2003)
Mathematical Macromodeling of Infilled Frames: State of the Art
P. G. Asteris;S. T. Antoniou;D. S. Sophianopoulos;C. Z. Chrysostomou.
Journal of Structural Engineering-asce (2011)
Prediction of the fundamental period of infilled RC frame structures using artificial neural networks
Panagiotis G. Asteris;Athanasios K. Tsaris;Liborio Cavaleri;Constantinos C. Repapis.
Computational Intelligence and Neuroscience (2016)
Seismic vulnerability assessment of historical masonry structural systems
P.G. Asteris;M.P. Chronopoulos;C.Z. Chrysostomou;H. Varum.
Engineering Structures (2014)
Mathematical micromodeling of infilled frames: State of the art
P. G. Asteris;Demetrios M Cotsovos;C. Z. Chrysostomou;A. Mohebkhah.
Engineering Structures (2013)
On the in-plane properties and capacities of infilled frames
C.Z. Chrysostomou;P.G. Asteris.
Engineering Structures (2012)
MASONRY FAILURE CRITERION UNDER BIAXIAL STRESS STATE
C. A. Syrmakezis;P. G. Asteris.
Journal of Materials in Civil Engineering (2001)
A macro-modelling approach for the analysis of infilled frame structures considering the effects of openings and vertical loads
Panagiotis G Asteris;Liborio Cavaleri;Fabio Di Trapani;Vasilis Sarhosis.
Structure and Infrastructure Engineering (2016)
Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures
Panagiotis G. Asteris;Mehdi Nikoo.
Neural Computing and Applications (2019)
Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials.
Panagiotis G. Asteris;Panayiotis C. Roussis;Maria G. Douvika.
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