The paper discusses penalised spline (P -spline) smoothing for hazard regression of multivariable survival data. Non-proportional hazard functions are fitted in a numerically handy manner by employing Poisson regression which results from numerical integration of the cumulative hazard function. Multivariate smoothing parameters are selected by utilizing the connection between P -spline smoothin...

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the ...

In this study, the Multivariate Adaptive Regression Splines (MARS) model is employed to create a data-driven prediction for bearing capacity of strip footing on rock mass subjected an inclined and eccentric load. The strengths masses are based Hoek-Brown failure criterion. To develop set training data in MARS, lower upper bound finite element limit analysis (FELA) carried out obtain numerical r...

This study presents a hybrid framework to predict stability solutions of buried structures under active trapdoor conditions in natural clays with anisotropy and heterogeneity by combining physics-based data-driven modeling. Finite-element limit analysis (FELA) newly developed anisotropic undrained shear (AUS) failure criterion is used identify the underlying mechanisms as well develop numerical...

In press. Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Diversity and Distributions. (A) Abstract 1 Current circumstances-that the majority of species distribution records exist as 2 presence-only data (e.g., from museums and herbaria), and that there is an established need 3 for predictions of...

This paper presents multiple methods for predicting heavy/mediumduty vehicle fuel consumption based on driving cycle information. A polynomial model, a black box artificial neural net model, a polynomial neural network model, and a multivariate adaptive regression splines (MARS) model were developed and verified using data collected from chassis testing performed on a parcel delivery diesel tru...

In this study, taking into account the Aksu Stream data, daily total precipitation (P) and mean flow (Q) values were using time lagged, 8 different Rainfall-Runoff models created runoff value estimated for future. The have been tried with methods performances compared process. Artificial Intelligence (AI) methods, Neural Networks (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS) Heuristic Re...

To model the trajectory of pandemic in Kuwait from February 24, 2020 to 28, 2021, we used two modeling procedures: Auto Regressive Integrated Moving Average (ARIMA) with structural breaks and Multivariate Adaptive Regression Splines (MARS), then mapped key breakpoints models set government-enforced interventions. The MARS model, as opposed ARIMA provides a more precise interpretation interventi...