Dr Mehravar has extensive research experience in the field of computational mechanics and experimental modelling applied to geotechnical engineering problems.
This includes multi-phase modelling of geo-structure in particular geo-energy infrastructure such as energy piles and foundations of offshore wind turbines and is experienced in the use of biomaterials for ground engineering/improvement.
She has worked on several research projects related to renewable energy and particularly on innovative and novel solutions for foundations of offshore wind turbines.
She is also interested in smart infrastructure including (i) the use and development of novel sensing technologies (fibre optic sensors) for ground engineering and (ii) development of innovative and efficient approaches for condition assessment of existing infrastructure and sustainable design of new infrastructure.
Dr Mehravar joined Aston University in 2017 as a Lecturer (Assistant Professor) in Civil Engineering at the College of Engineering and Physical Science. Prior to that, she was a postdoctoral research fellow at the Loughborough University.
She has published more than 20 research papers in prestigious journals and international conferences.
Areas of Expertise (11)
TA Engineering (General). Civil engineering (General)
Higher Education Academy: FHEA 2019
University of Greenwich: PhD, Geotechnical Engineering 2015
- Fellow of the Higher Education Academy (FHEA)
Nearshore Contamination Monitoring in Sandy Soils Using Polymer Optical Fibre Bragg Grating Sensing SystemsSensors
2022 Civil engineering assets and geo-structures continually deteriorate during their lifetime, particularly in harsh environments that may be contaminated with corrosive substances. However, efficient and constant structural health monitoring and accurate prediction of the service-life of these assets can help to ensure their safety, performance, and health conditions and enable proper maintenance and rehabilitation. Nowadays, many of the largest cities throughout the world are situated in coastal zones, leading to a dramatic increase in the construction of nearshore geo-structures/infrastructures which are vulnerable to corrosion attacks resulting from salinity contamination. Additionally, seawater intrusion can threaten the quality and the sustainability of fresh groundwater resources, which are a crucial resource in coastal areas. To address these issues, detection of salinity in soil utilizing a novel polymer optical fibre Bragg grating (POFBG) sensor was investigated in this research. Experiments were carried out at different soil water contents with different salinities to assess the sensor’s response in a representative soil environment. The sensitivity of the POFBG sensor to salinity concentrations in water and soil environment is estimated as 58 ± 2 pm/%. The average standard error value in salinity is calculated as 0.43% for the samples with different soil water contents. The results demonstrate that the sensor is a promising and practical tool for the measurement and monitoring with high precision of salinity contamination in soil.
Soil water content measurement using polymer optical fibre Bragg gratingsProceedings of the Institution of Civil Engineers-Smart Infrastructure and Construction
2022 Measuring soil water content is crucially important and can affect soil strength, which is a key parameter in the analysis, design and monitoring of geo-structures. In this study, an optical fibre Bragg grating sensor inscribed in polymer optical fibre was developed, and for the first time, its ability to measure soil water content was investigated. The sensitivity of the sensor to different values of gravimetric soil water content under the different compaction conditions of loose and normal compaction was tested. The effect of soil temperature on the sensor’s performance was considered. To assess the sensor’s implementation, accuracy and reliability, a commercial soil water content probe (SM150), which measures volumetric soil water content was employed. The results indicate that the developed sensor, when calibrated correctly, is able to provide detailed data on any minor variation of soil water content (e.g. 0.5%) with high precision. The outcomes of this study define an additional capability of the polymer optical fibre Bragg grating sensors, which is significantly important for the long-term performance monitoring of geo-structures.
A Back-Analysis Technique for Condition Assessment of Ballasted Railway TracksAdvances in Transportation Geotechnics IV
2022 Track substructure is a key component of railway transportation systems. Similar to the built environment of other surface transportation systems, track substructures are subjected to ageing and deterioration. This frequently leads to failure and collapse of systems and imposing costly repairs and maintenance. Further, limited knowledge about the substructure condition leads to employing inefficient, time-consuming, and expensive maintenance. As the importance of time and budget limitation, there is a need to develop more time and cost-efficient techniques for frequent condition assessment of the existing railway substructures. Falling weight deflectometer (FWD) is recognised as an effective non-destructive test (NDT) for surveying the ballasted railway substructures through the back-analysis process, including a forward analysis of track substructure and an optimisation method. This paper presents a novel hybrid back-analysis technique, including artificial neural network (ANN) and ant colony optimisation for the continuous domain (ACOR) to backcalculate substructure layer moduli of railway track. To this aim, a dynamic finite element (FE) model is developed to generate a reliable dataset which is covering various layer moduli for ANN training. ACOR is employed as an optimisation tool to optimise estimated layer moduli (ANN’s input). Furthermore, a validation study has been conducted using the developed FE model with back-analysed layer moduli values to evaluate the developed technique’s performance. The validation study results show that use of ANNs incorporates ACOR results in excellent performance and robustness of the developed back-analysis technique. The hybrid ANN-ACOR back-analysis technique is a computationally efficient method with no dependency on seed modulus values.