Prediction of Primary Deformation Modulus Based on Bearing Capacity: A Case on Forest Road with a Light Falling Weight Deflectometer Zorn ZFG 3000 GPS

Abstract

Bearing capacity and compaction are among the most important and frequently used geotechnical parameters in road construction. The aim of this study was to determine the possibility of predicting the value of the primary deformation modulus E1 (obtained from measurements using a static plate load test—PLT) based on measurements with a Zorn light falling weight deflectometer (LFWD), type ZFG 3000 GPS, with a drop weight of 10 kg. A regression analysis was performed on 245 bearing capacity measurements that were taken on 46 forest road sections with various road surfaces. Different regression models were tested, from linear to logarithmic, polynomial, exponential and power models, but excluding polynomials of fourth and higher degree. The results showed that the prediction of E1 values (PLT) from the dynamic deformation modulus values Evd (LFWD) was possible. However, the reported unsatisfactory strength of the relationship between the two parameters was associated with a high risk of error (r = 0.64, R2 = 0.41, Se = 49.78). Neither the use of more complex non-linear regression models, nor the use of multiple regression by introducing an additional estimator in the form of the s/v ratio, significantly improved estimation results. The quality of the prediction of the E1 value was not constant. It varied, depending on the type of forest road, the use of geosynthetic reinforcement and the type of road subgrade. During the study, it was also found that the quality of the prediction of the E1 value could be improved by limiting the range of Evd values tested from above. It is advisable to continue this type of research, as the obtained results could form the basis for future development of national standards for the use of LFWDs to control the bearing capacity and compaction of forest road pavements.

Review of Sensor-Based Subgrade Distress Identifications

“…Ground-penetrating radar (GPR) [22][23][24][25][26] Total station [27][28][29][30][31] InSAR [32][33][34][35][36] Distributed optical fiber [37][38][39][40][41] Subgrade frost heaving Ground-penetrating radar (GPR) [42][43][44][45] Multi-sensor embedding [46][47][48][49][50] Weak subgrade Falling weight deflectometer (FWD) [51][52][53][54][55][56]…”

“…Heejun Lee et al [54] used a falling weight deflectometer to evaluate the strength of subgrade layers and the potential distresses. Sylwester M. Grajewski [55] determined the predicted primary deformation modulus according to the measurement results of a lightweight drop weight of 10 kg. After collecting deflection data, regression analysis was performed on 245 measured data points, which were carried out on 46 different road sections.…”

Sensors for Digital Transformation in Smart Forestry

“…To illustrate with a specific example from forestry, assessing forest road trafficability can employ various technologies. These include the falling weight deflectometer [19], soil moisture measurements [20], and integrating these methods with different terrain models [21]. While these technologies are established and effective for evaluating road trafficability, AI introduces an advanced approach.…”