Dr Martinez-Vazquez received his PhD from the National University of Mexico (UNAM) in 2006. He moved to the UK in 2007 following an invitation from an industrial firm which specialises in wind tunnel studies. In 2008 he was appointed Research Fellow by the School of Engineering at The University of Birmingham and in 2011 he undertook a lectureship at The University of Liverpool. In 2012 he returned to Birmingham for his current position as Design Tutor.
Dr Martinez-Vazquez has long experience in the construction industry. Previous to his PhD studies he participated in the design and supervision of over 50 structures located in South America and the United States. He is familiarised with building codes and engineering practices from Europe and overseas.
He has published 22 papers. His research interests are varied but include structural dynamics, fluid dynamics, wind-tunnel techniques, computational modelling, artificial intelligence, and image recognition techniques. He is also keen to undertake research in other fields of science such as agriculture, biomechanics, teaching and education.
He is graduate member of the IStructE and has recently obtained a Certificated in Higher Education.
Dr Martinez-Vazquez teaches all levels of Civil Engineering. The modules he is currently involved are Construction Design and professional Skills A, B, Electronics Electrical and Computer Systems, Engineering Design, BEng Design Project, Structural Engineering Design, MEng Design Project, Civil Engineering Research Project, and Engineering Structural Dynamics.
These are some examples of the research undertaken by Dr Martinez-Vazquez:
Experimental: The Flight of Windborne Debris - An Experimental, Analytical and Numerical Investigation
A series of flat square plates were tested in static positions and during autorotation by embedding a number of pressure sensors and portable data loggers within the thickness of the plates. This is a novel technique that allowed for the first time to observe the time variation of surface pressures on moving plates. The measured force fields revealed the existence of complex flow structures that cannot be predicted by using standard experimental techniques. The proposed experimental technique also opened the possibility of correlating the real pressure and flow fields on rotating plates which up to date has been studied separately.
Structural Dynamics: Design Spectra for Wind Loading
Design spectra are normally applied in Seismic Engineering where it is assumed that the inertial forces induced by the horizontal accelerations acting at base of the structure are fully correlated. In the case of wind loading the amount of energy imparted by the wind to the structural system can be estimated for point-like structures as well as for large areas by considering suitable spatial correlation laws. In addition the use of generalised techniques allows considering the dynamic response of single oscillators whose ensemble response constitutes a design spectrum which in turn can be used to carry out modal analyses. Unlike similar spectral approaches design spectra would not restricted to cases in which the total response is mainly given by the fundamental mode of vibration but these would be applicable to the analysis of multiple degree of freedom systems where the contribution of higher order modes to the total dynamic response is important.
Design Spectrum for Wind Load
Artificial Neural Networks and Image Recognition: Wind Field Reproduction based on ANN and Conditional Simulation
Image recognition techniques (IR) allow the representation of one-dimensional wind time series as two-dimensional plots. This can be achieved by projecting the components of a one dimensional data series into a two dimensional space. The numerical version of several RPs can be assembled to form a subspace here referred to as eigen-space since it follows an eigen-value analysis that allows the redefinition of the data series in terms of short dimension vectors (Ω). IR allows the reconstruction of RPs based on their associated vector Ω. The correspondence RP-Ω can be mapped using an artificial neural network (ANN). The prediction of encoded time series through ANN permits their reconstruction in time domain because the process is reversible. ANN evolved from artificial intelligence to constitute a relatively modern tool with the objective of simulating the natural process of human learning.
Encoding of time series into RP through IR, for the training of ANN
The process shown in the figure was used to identify the characteristics of wind at specific locations above ground level which were then combined with the algorithm Conditional Simulation in order to constitute correlated wind fields.
Agriculture: Predicting Wheat Lodging at Large Scales
The motion of a series of idealised wheat plants in the time domain subject to the application of a wind induced force were examined. A stochastic process representing the wind loading at different points over an imaginary field of wheat was generated. The plants were then modelled as damped harmonic oscillators and the wind induced response compared to the resistance offered by the root/soil and the plant stem. A comparison with a previous approximate method was also undertaken and a simple way to transform the approximate solutions to the full solution outlined here was provided. This computational method provides an alternative approach enabling plant failure over large populations to be predicted.
Free vibration of wheat plants under several conditions for stability