Search

Dynamic cone resistance (DCR) is a recently introduced soil resistance index that has the unit of stress. It is determined from the dynamic response at the tip of an instrumented dynamic cone penetrometer. However, DCR evaluation is generally a manual, time‐consuming, and error‐prone process. Thus, this study investigates the feasibility of determining DCR using a stacked ensemble (SE) machine learning (ML) model that utilizes signals obtained from dynamic cone penetration testing. Two ML experiments revealed that using only force signals provides more accurate predictions of DCR. In addition, the SE technique outperformed the base learning algorithms in both cases. Overall, the findings suggest that ML techniques can be used to automate the analysis of DCR with force and acceleration signals. [ABSTRACT FROM AUTHOR]

The resilient modulus of subgrade should be estimated for pavement designs that undergo repetitive loading from vehicular movement. In this study, an in-situ modulus detector (IMD) with flat, wedge, and cone tips is employed to estimate the resilient modulus. The specimens with three different levels of water content are compacted, and the dynamic responses for two hammer drop heights are analysed. For all specimens, elastic waves are acquired to estimate the shear modulus and Poisson's ratio using bender and piezo-disk elements. The resilient modulus estimated from the IMD is compared with the lightweight deflectometer (LWD) modulus. The experimental results show a stronger relationship between the flat and wedge tips than between the cone and wedge tips. Furthermore, higher drop heights result in strong relationships between the resilient, shear, and LWD moduli. This study proposes an intrinsic design and verification of an IMD for reliable resilient modulus estimation in pavement design. [ABSTRACT FROM AUTHOR]

Elastic waves obtained from transducers are widely used to monitor the strength and integrity of cementitious materials. A pair of bender elements (BEs) and piezo disk elements (PDEs) embedded in a cuboid mould are applied to monitor elastic waves passing through a cementitious material known as a flowable fill at early age. The flowable fills used in this study are mixed with calcium sulfoaluminate cement, sand, silt, fly ash, water, and an accelerator. Compressional and shear waves acquired from the PDEs and BEs, respectively, are observed for up to 72 hrs. Results show that the compressional and shear wave velocities increased with curing time. The sensors can be used effectively to monitor the hardening characteristics of flowable fills at early age, and the BEs are particularly suitable for evaluating the hardening conditions of flowable fills as a result of the wide variation in shear wave velocities.

Resilient moduli are widely used in mechanistic analyses to predict distresses in pavement structures. The objective of this study is to develop an in-situ modulus detector (IMD) to evaluate the resilient moduli of subgrades based on various influencing factors. The IMD consists of a tip module, a driving rod, and a hammer-drop system. In the tip module, a load cell and an accelerometer are installed to measure the dynamic responses of force and acceleration. The resilient modulus is calculated from the deviator stress and the recoverable strain determined using the IMD. A weathered soil is prepared for the compacted specimens that are used in the dynamic penetration tests. These dynamic penetration tests are conducted with the IMD on four compacted specimens with different water contents under seven different hammer-drop heights. The experimental results demonstrate that the maximum amplitudes of the force and displacement depend on the drop height of the hammer and the water content of the compacted specimens. The resilient modulus profiles obtained from using the IMD can be influenced by the hammer energy, water content, and dry density of soils. The IMD may be a promising field-testing device to evaluate the in-situ resilient moduli of subgrades. [ABSTRACT FROM AUTHOR]