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Reloading Modulus Estimation Based on Static and Dynamic Plate Load Tests Carried out on Unpaved Forest Roads

Pawłowski Michał, Grajewski Sylwester M., Węgliński Szymon

Advances in Science and Technology. Research Journal

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2024

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Vol. 18, no 4

250–264

EN

This paper presents the results of comparative studies of strain modulus from static (PLT) and dynamic (LWD) plate testing. The tests were conducted on 9 sections of forest roads with different surfaces made of unbound aggregates. They produced 140-element sets of results, including values of reloading modulus (E2) and dynamic modulus of deformation determined using 10 and 15 kg drop weights (Evd10 and Evd15). An attempt was made to determine the relationship between the values of the moduli from tests with LWD loads (10 or 15 kg) and PLT, which would allow to determine the values of reloading modulus based on the dynamic modulus values. The analysis of the test results revealed that the values of the dynamic moduli are characterized by lower variability than those obtained from static testing and that from the engineering point of view there is no significant relationship between the sets of results of the subgrade deformability tests made with dynamic and static plates. The analysis of the results confirmed a simple relationship that allows for a qualitative assessment of subgrade deformability defined by the values of reloading modulus PLT tests based on the results of LWD tests with a 10 kg drop weight. The assessment error did not exceed 7% in this case. An analogous relationship was revealed for the results of LWD tests with a 15 kg drop weight. In this case, the assessment error did not exceed 6%. The results of the LWD tests can be used to provide a qualitative assessment of the deformability of subgrade, but the PLT tests are required for its quantitative assessment.

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A robust inversion of logging-while-drilling responses based on deep neural network

Zhu Gaoyang, Gao Muzhi, Wang Bin

Acta Geophysica

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2024

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Vol. 72, no. 1

129--139

EN

Resistivity inversion plays a significant role in recent geological exploration, which can obtain formation information through logging data. However, resistivity inversion faces various challenges in practice. Conventional inversion approaches are always time-consuming, nonlinear, non-uniqueness, and ill-posed, which can result in an inaccurate and inefficient description of subsurface structure in terms of resistivity estimation and boundary location. In this paper, a robust inversion approach is proposed to improve the efficiency of resistivity inversion. Specifically, inspired by deep neural networks (DNN) remarkable nonlinear mapping ability, the proposed inversion scheme adopts DNN architecture. Besides, the batch normalization algorithm is utilized to solve the problem of gradient disappearing in the training process, as well as the k-fold cross-validation approach is utilized to suppress overfitting. Several groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed inversion scheme. In addition, the robustness of the DNN-based inversion scheme is validated by adding different levels of noise to the synthetic measurements. Experimental results show that the proposed scheme can achieve faster convergence and higher resolution than the conventional inversion approach in the same scenario. It is very significant for geological exploration in layered formations.

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Influence of pavement moisture content on the load-bearing capacity of forest road

Grajewski S. M.

Infrastruktura i Ekologia Terenów Wiejskich

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2016

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nr IV/2

1451--1462

EN

The aim of the study was to analyse the interaction between pavement moisture content and load-bearing capacity of an unpaved forest road. The selected experimental road sector was divided into three sections (A, B and C), which were flooded with three different amounts of water 10 mm (section A), 20 mm (section B) and 30 mm (section C), compared with the intense rainfall. Three series of tests were conducted at each section: prior to flooding (1st day of measurements), during the first 24 hours after flooding (2nd day) and during the next 24 hours after flooding (3rd day of measurements). Moisture content of structural layers of the road (surface course, base course and subbase course) were determined and the measurements using a light weight deflectometer (Evd, s/v) and a static plate (E1 , E2 , Io ) were conducted. Recorded averaged results clearly indicate a negative effect of an increase in pavement layers moisture content (e.g. resulting from heavy rainfall) on the forest road carrying capacity and on compaction parameters of its layers. On the third day of the measurements a decrease in the analysed modulus, on average between 16% (E2 ) and 25% (Evd) was observed, but a decrease in compaction by 16% (s/v) and 4% (Io).