Effects of embedding depth and load eccentricity on lateral response of offshore monopiles in dense sand: A centrifuge study

Two model piles with outer diameter D = 50 mm are loaded laterally at 100×g in a large-beam geotechnical centrifuge. The normal strains on both the tensile and compressive sides are measured using fibre Bragg gratings. An incremental method is introduced to define the pivot point. The testing and analytical program enables the effect of the embedding depth and load eccentricity to be quantified. The key findings are as follows. 1) The piles generate asymmetric tensile and compressive strains during bending, and the tension-compression asymmetry becomes more pronounced at the pile toe and for shorter piles. 2) The piles transition from flexure to rotation as the embedding depth is decreased from 9D to 3D, where the uniqueness of the ground-level rotation and deflection (θg–yg) relationship disappears. 3) The reaction and deflection (P–y) relationship flattens with increasing embedding depth but seems independent of the load eccentricity.

Clustering Algorithm for Vehicle’s Driving Data Feature based on Integrated Navigation

The clustering result of K-means clustering algorithm is affected by the initial clustering center and the clustering result is not always global optimal. Therefore, the clustering analysis of vehicle’s driving data feature based on integrated navigation is carried out based on global K-means clustering algorithm. The vehicle mathematical model based on GPS/DR integrated navigation is constructed and the vehicle’s driving data based on GPS/DR integrated navigation, such as vehicle acceleration, are collected. After extracting the vehicle’s driving data features, the feature parameters of vehicle’s driving data are dimensionally reduced based on kernel principal component analysis to reduce the redundancy of feature parameters. The global K-means clustering algorithm converts clustering problem into a series of sub-cluster clustering problems. At the end of each iteration, an incremental method is used to select the next cluster of optimal initial centers. After determining the optimal clustering number, the feature clustering of vehicle’s driving data is completed. The experimental results show that the global K-means clustering algorithm has a clustering error of only 1.37% for vehicle’s driving data features and achieves high precision clustering for vehicle’s driving data features.

Incremental Method vs Split Conditions: Discussing the Similarities Between Reserves Evaluation and a Madoff Scheme

After the oil price crashes in 2014 & 2020 several M&A deals ended up in legal debates because operators cancelled major projects or infills wells that were booked in the "probable" reserves only. This document challenges the compatibility between the deterministic incremental reserve assessment method (PRMS2018, chapter 4.2.1.3), and the concept of split condition (PRMS2018 chapter 2.2.0.3), which is not allowed for reserves booking under PRMS. With a few examples, we explain why the incremental method may be misleading investors, if used wrongly. Policies, stock market requirements, or simply the understanding of reserves guidelines may differ from one company to another. Many filers and auditors are still keen on using the deterministic incremental approach. This method consists in "defining discrete parts or segments of the accumulation that reflect high, best, and low confidence regarding the estimates of recoverable quantities under the defined development plan". In principle, this should give similar result to the widely accepted scenario method (PRMS2018, chapter 4.2.1.3) but in reality, major discrepancies are observed. Some reserve evaluation may also become misleading for banks, investors, or even for good asset management. I many cases, the estimation of recoverable volumes is reasonable, but it does not match the company CAPEX requirements, affecting corporate cash flow as well as potential Reserves Based Lending (RBL) requirements. In another case, the 1P case will be robust, but the 2P may be grossly overestimated, affecting M&A or share price. "Reserves guidelines are principle based" this has recently become a very fashionable statement in the context of SEC bookings. Similar discussions will also occur when reviewing PRMS reports. However, different interpretations for keywords such as "Project", "Spit condition", or "FID" should not prevent the evaluator to provide a reliable reserves estimation to investor or company management. This document questions the threshold where ethics disappears, and a Madoff scheme may become legal.

Smart Contact Tracing and Classifier System for Covid-19 Cases

The growing shreds of evidence and spread of COVID-19 in recent times have shown that to effortlessly and optimally tackle the rate at which COVID-19 infected individuals affect uninfected individuals has become a pressing challenge. This demands the need for a smart contact tracing method for COVID-19 contact tracing. This paper reviewed and analysed the available contact tracing models, contact tracing applications used by 36 countries, and their underlined classifier systems and techniques being used for COVID-19 contact tracing, machine learning classifier methods and ways in which these classifiers are evaluated. The incremental method was adopted because it results in a step-by-step rule set that continually changes. Three categories of learning classifier systems were also studied and recommended the Smartphone Mobile Bluetooth (BLE) and Michigan learning classifier system because it offers a short-range communication that is available regardless of the operating system and classifies based on set rules quickly and faster.

The effect of the roller profile oncave-in defect in reshaping processby considering nonlinear combine strain hardening

The reshaping process of pipes is an important method in producing non-circular pipes. Desired profile products are produced by passing round pipe through the rotating rollers. Cave-in defect is one of the common defects in the reshaping process. Roller design issues can decrease this kind of defect. In this paper, a method based on the slab method and the incremental plasticity has been presented to the numerical study of a 2D reshaping process. For investigating the Cave-in defect, the contact model has been developed. The concept of element elongation has been introduced to increase the accuracy of the contact model. Based on the presented method, numerical software has been developed to simulate the 2D reshaping process. Elastic-plastic equations for this subject have been driven based on the incremental method, J yielding criterion, and non-linear combined hardening. The effects of the radius of the roller profile on cave-in defects have been investigated by using the presented software (DARF). A set of experiments has been conducted in a forming station to verify the results. Results show that the presented model has higher accuracy than the Abaqus commercial software in predicting the cave-in defect. Based on the results of the model, the local increase of yielding stress directly affects the cave-in defect. Also, a meaningful relationship between the radius of the roller and the amount of the cave-in has been observed.

The Modified Increment Method for Eigenspace Model

Eigenspace is a convenient way to represent sets of observations with widespread applications, so it is necessary to accurately calculate the eigenspace of data. With the advent of the era of big data, the increasing and updating of data bring great challenges to the solution of eigenspace. Hall, et al. [1], proposed that the incremental method could update the eigenspace of data online, which reduces computational costs and storage space. In this paper, the updating coefficient of the sample covariance matrix in an incremental method is modified. Numerical analysis shows that the modified updating form has better performance.