Acoustic Emission Testing of Bolted Anchors used in Rope Access and Rock Climbing
Description
This data set relates to two publications, the first being 'Acoustic Emission Testing of Bolted Anchors used in Rope Access and Rock Climbing' by Atkinson et al, awaiting publication in Construction and Building Materials. The second is a conference paper presented at the 37th Conference of the European Working Group on Acoustic Emission. Titled 'Exploring Acoustic Emission for Integrity Assessment of Bolted Anchors used in Rock Climbing and Rope Access', which can found on NDT.net. Both contain overlapping details of the experimental methodology, sample, and materials used. With the journal publication being much more substantive, the abstract of which can be seen below. Resin Bolts and Expansion Bolts are critical safety elements in rope access and rock climbing, the unexpected failure of a bolt could lead to serious injury or death. There are currently no sophisticated method for inspecting or evaluating bolts, the current practice is the simple application of a 6 kN tensile test load. Additionally, while literature exists on the topic of mechanical testing and failure analysis of bolts, no literature has applied advanced monitoring methods. The formation of micro/macro cracks in a material releases an elastic wave known as an Acoustic Emission (AE), which can be used to quantify, characterise, and locate damage. To explore the potential application of AE to resin and expansion bolts, three experiments were undertaken, monotonic loading, Felicity testing, and fatigue cycling. The results showed that resin bolts outperformed expansion bolts, maintaining their stiffness at higher loads, and exhibiting less evidence of damage accumulation at low loads. Calculated Felicity ratios and improved b-values showed that lasting plastic damage occurs at loads higher than 7 kN for both bolt types, combined with the fatigue results it is unlikely that fall-arrests and the standard 6 kN inspection load are detrimental to the integrity of a bolt in 40 MPa concrete. AE event locations are discussed for the fatigue tests, where it is observed that the density of events decreases further away from the bolt. Overall, these results provide an initial demonstration of the level of insight which can be attained through the application of AE to bolt integrity assessment. In this dataset is all the experimental data, and the code used to prepare, analyse, and visualise it. Instructions on how to easily access it can be seen in the README.
Files
Steps to reproduce
Details of the experimental methodology can be found in the attached papers. The data came from three sources, AE data captured by the MISTRAS Express-8, Load & Displacement Data captured directly and by the Express-8, lastly the event location data is captured by the MISTRAS Express-8 but extracted using a different method. The Express-8 exports AE in the form of a unreadable DTA binary file. To allow for easy handling these are converted to .csv files using some propriety code. The load/displacement data is captured in higher resolution directly from the machine, and aligned with the AE data using the load/displacement data captured as parametrics. Lastly the event location data is copy and pasted directly from the AEWIN user interface, and then cleaned up with code. Python was then used to clean, organise, and analyse the data. Storing the unified data from each test as a Pickle. A Pickle is essentially a stored python class/object. Each Pickle represents a tensile test of one bolt. Each object contains variables for: the type of bolt, the type of test, the AE waveform sample rate, the threshold in dBAE and volts, Peak-Definition-Time (PDT), Hit-Lockout-Time (HLT), the Hit-Definition-Time (HDT), as well as some variables specific to that type of test. It also has Pandas Dataframes containing: some test parameters, the Hit-Driven-Data (HDD), Time-Driven-Data (TDD), the data from the load machine, the event locations (If Fatigue), and lastly all the AE features extracted from the waveforms, and the waveforms themselves. Attached is also all the code used throughout the project. However this code was never intended for use by anyone but the authors, and so is unlikely to be helpful. It is included for completeness and transparency. It is instead recommended that any user simply import the Pickle and work with it in their own way. Examples of how to work with Pickles can be found here (https://docs.python.org/3/library/pickle.html#examples).
Institutions
- Cardiff UniversityWales, Cardiff
Categories
Funders
- MISTRAS Group Ltd.