... prediction models are based on a single machine learning method, which is not generalised enough and has a low accuracy. In view of this, a mathematical model for engineering cost prediction is constructed by combining a random forest algorithm, the ridge regression algorithm and the extreme gradient...

..., 2006) 3 y = ∑ j = 1 m F [ X , f ( X ) , a j ] + a 0 (corresponding author: a.ahangarasr@salford.ac.uk) 06 07 2021 15 07 2021 ICE Publishing: All rights reserved 2020 geotechnical engineering mathematical modelling stress...

... non-linear representations suitable for challenging data sets. (corresponding author: a.mikhailova18@imperial.ac.uk) 15 12 2019 14 09 2020 ICE Publishing: All rights reserved 2019 bridges mathematical modelling statistical analysis...

... faced by the passenger at the time of travel. GBDTs are used to predict mode choice, with parameter values selected using sequential optimisation. Continuous choice probabilities are obtained for all modes, and the model fit is primarily assessed using log-likelihood loss. mathematical modelling...

.... (corresponding author: dhl@imperial.ac.uk) bridges mathematical modelling statistical analysis C_b scaled and centred observation for strain records k moving window half-width l batch window half-width M_s statistical...

.... This paper discusses the advancements and challenges in high-performance computing (hardware, algorithms, software, and visualisation techniques) in achieving the possibility of real-time city-scale simulations. information technology infrastructure planning mathematical modelling Traditional...