Advances in pile integrity testing

Abstract

For decades, the low‐strain impact integrity testing using a hammer blow is well established as a method of quality assurance for various pile types. However, this method has its limitations. Our research and development focuses on improving the excitation signal using a shaker system in contrast to the standard hammer method. Another approach is to increase the amount of sensors used during testing. The purpose is to identify the direction of wave propagation that gives advantages under difficult conditions, such as piles below structures. Pile integrity testing using a shaker system was performed on two 11‐m‐long piles of 90 cm in diameter. While one pile was intact, the other one showed a flaw at approximately 3.5 m below pile top, which was confirmed by standard pile integrity testing in 2012. A logarithmic sweep between 500 Hz and 1 KHz of 0.1 s was used as the input signal, being vertically injected into the pile. Prior to that, simulations on similar pile geometries showed that the depth of the pile toe as well as flaws within the pile can be extracted by applying regularised deconvolution. The result is the impulse response in the time domain. The application of deconvolution on the measured signals shows that it is possible to identify the pile length, but it is more difficult to clearly extract the flaw’s position in the pile. Additional digital signal processing techniques and the improvement of the regularised deconvolution method and the experimental setup need to be investigated. Another way to improve the pile integrity testing method is to use a multi‐channel sensor arrangement. By arranging several accelerometers vertically along the accessible part of the pile shaft, it is possible to distinguish between downward and upward travelling waves. Furthermore, it is possible to estimate the unknown wave speed, which gives the possibility of more accurate pile length calculations. The method was evaluated successfully during a measurement campaign of a slab foundation with subjacent piles. In 20 of 28 cases, the pile length could be detected accurately.

Classification of Low-Strain Foundation Pile Testing Signal Using Recurrent Neural Network

“…Therefore, the measured particle velocity v indirectly reflects the change of pile impedance Z. Under the condition that the tested object is the model pile, the change of pile section area affects the integrity of the pile [33][34][35][36].…”

Condition Assessment of Foundation Piles and Utility Poles Based on Guided Wave Propagation Using a Network of Tactile Transducers and Support Vector Machines

“…While visual inspection is undoubtedly one of the oldest assessment methods, it is limited to accessible areas and surface damage, and like sounding and resistance drilling, its reliability and accuracy is highly dependent on the experience of the operator [ 2 ]. As an alternative to these limited low-tech condition assessment methods, guided wave-based methods, such as pile integrity testing, are established testing methods for concrete piles and deep foundations that provide objective quantitative data, and are able to potentially detect internal damage and evaluate the health condition of non-accessible areas such as embedded sections of foundation piles and utility poles [ 3 , 4 , 5 ]. In guided wave testing, stress waves are generated in a structure, typically through a sudden pressure or deformation caused by an impact excitation, which subsequently propagate through the structure similar to the propagation of sound waves through air [ 6 ].…”

Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques

“…Over the years, research efforts on improving the LSPIT method have been focused on two separate directions: (1) theoretical analysis and understanding of LSPIT mechanism and (2) intelligent and automatic interpretation of test results. While the first direction is important and has attracted much research interests (e.g., [ 14 ]), our paper is concerned with the second direction, i.e., automatic interpretation of LSPIT signals. Toward intelligent interpretation of LSPIT signals, a limited number of studies have been on using advanced signal processing and artificial intelligence (pattern recognition) techniques for LSPIT signal analysis.…”