A new approach to modeling the behavior of frozen soils

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordIn this paper a new approach is presented for modeling the behavior of frozen soils. A data-mining technique, Evolutionary Polynomial Regression (EPR), is used for modeling the thermo-mechanical behavior of frozen soils including the effects of confining pressure, strain rate and temperature. EPR enables to create explicit and well-structured equations representing the mechanical and thermal behavior of frozen soil using experimental data. A comprehensive set of triaxial tests were carried out on samples of a frozen soil and the data were used for training and verification of the EPR model. The developed EPR model was also used to simulate the entire stress-strain curve of triaxial tests, the data for which were not used during the training of the EPR model. The results of the EPR model predictions were compared with the actual data and it was shown that the proposed methodology can extract and reproduce the behavior of the frozen soil with a very high accuracy. It was also shown that the EPR model is able to accurately generalize the predictions to unseen cases. A sensitivity analysis revealed that the model developed from raw experimental data is able to extract and effectively represent the underlying mechanics of the behavior of frozen soils. The proposed methodology presents a unified approach to modeling of materials that can also help the user gain a deeper insight into the behavior of the materials. The main advantages of the proposed technique in modeling the complex behavior of frozen soil have been highlighted

Reference ranges for Doppler indices of umbilical and fetal middle cerebral arteries and cerebroplacental ratio: systematic review.

OBJECTIVE: To assess studies reporting reference ranges for umbilical artery (UA) and fetal middle cerebral artery (MCA) Doppler indices and cerebroplacental ratio (CPR), using a set of predefined methodological quality criteria for study design, statistical analysis and reporting methods. METHODS: This was a systematic review of observational studies in which the primary aim was to create reference ranges for UA and MCA Doppler indices and CPR in fetuses of singleton gestations. A search for relevant articles was performed in MEDLINE, EMBASE, CINAHL, Web of Science (from inception to 31 December 2016) and references of the retrieved articles. Two authors independently selected studies, assessed the risk of bias and extracted the data. Studies were scored against a predefined set of independently agreed methodological criteria and an overall quality score was assigned to each study. Linear multiple regression analysis assessing the association between quality scores and study characteristics was performed. RESULTS: Thirty-eight studies met the inclusion criteria. The highest potential for bias was noted in the following fields: 'ultrasound quality control measures', in which only two studies demonstrated a comprehensive quality-control strategy; 'number of measurements taken for each Doppler variable', which was apparent in only three studies; 'sonographer experience', in which no study on CPR reported clearly the experience or training of the sonographers, while only three studies on UA Doppler and four on MCA Doppler did; and 'blinding of measurements', in which only one study, on UA Doppler, reported that sonographers were blinded to the measurement recorded during the examination. Sample size estimations were present in only seven studies. No predictors of quality were found on multiple regression analysis. Reference ranges varied significantly with important clinical implications for what is considered normal or abnormal, even when restricting the analysis to the highest scoring studies. CONCLUSIONS: There is considerable methodological heterogeneity in studies reporting reference ranges for UA and MCA Doppler indices and CPR, and the resulting references have important implications for clinical practice. There is a need for the standardization of methodologies for Doppler velocimetry and for the development of reference standards, which can be correctly interpreted and applied in clinical practice. We propose a set of recommendations for this purpose. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd

Developing constitutive models from EPR-based self-learning finite element analysis

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.A constitutive model that captures the material behaviour under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self-learning simulation (SelfSim). Self-learning simulation is an inverse analysis technique that extracts material behaviour from some boundary measurements (e.g., load and displacement). In the heart of the self-learning framework is a neural network which is used to train and develop a constitutive model that represents the material behaviour. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of self-learning simulation within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behaviour of a system. Two strategies of material modelling have been considered in the self-learning simulation-based finite element analysis. These include a total stress-strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress-strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR-based self-learning finite element method. The EPR-based self-learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.The authors would like to acknowledge the financial support (PhD scholarship) from the Ministry of Higher Education of Iraq