Effects of vertical loading on lateral screw pile performance

The offshore wind energy sector faces new challenges as it moves into deeper water deployment. To meet these challenges, new and efficient foundation solutions are required. One potential solution is to upscale onshore screw piles but they require verification of performance for new geometries and demanding loading regimes. This paper presents a three-dimensional finite-element analysis investigation of screw pile behaviour when subjected to combined vertical and lateral loading in sand. In the investigation, the screw pile length and helical plate diameter were varied on piles with a fixed core diameter while subjecting the piles to combined axial and lateral loading. The results were compared with results from straight shafted piles with the same core diameter. The results of the analysis revealed that vertical compression loads increased the lateral capacity of the screw piles whereas vertical uplift loads marginally reduced the lateral capacity. The downside of this enhanced lateral capacity is that the screw piles experience higher bending moments. This suggests that, when using screw piles for offshore foundation applications, structures should be designed to maintain axial compressive loads on the piles and induced bending moments need to be adequately assessed when deciding on appropriate structural sections. </jats:p

Forecasting of Air Maximum Temperature on Monthly Basis Using Singular Spectrum Analysis and Linear Autoregressive Model

In this research, the singular spectrum analysis technique is combined with a linear autoregressive model for the purpose of prediction and forecasting of monthly maximum air temperature. The temperature time series is decomposed into three components and the trend component is subjected for modelling. The performance of modelling for both prediction and forecasting is evaluated via various model fitness function. The results show that the current method presents an excellent performance in expecting the maximum air temperature in future based on previous recordings

Prediction and Forecasting of Maximum Weather Temperature Using a Linear Autoregressive Model

This paper investigates the autoregressive (AR) model performance in prediction and forecasting the monthly maximum temperature. The temperature recordings are collected over 12 years (i.e., 144 monthly readings). All the data are stationaries, which is converted to be stationary, via obtaining the normal logarithm values. The recordings are then divided into 70% training and 30% testing sample. The training sample is used for determining the structure of the AR model while the testing sample is used for validating the obtained model in forecasting performance. A wide range of model order is selected and the most suitable order is selected in terms of the highest modelling accuracy. The study shows that the monthly maximum temperature can accurately be predicted and forecasted using the AR model