Albert Alexander Rodger

Modelling of Ground Moling Dynamics by an Impact Oscillator with a Frictional Slider

This paper describes current research into the mathematical modelling of a vibro-impact ground moling system. Due to the structural complexity of such systems, in the first instance the dynamic response of an idealised impact oscillator is investigated. The model is comprised of an harmonically excited mass simulating the penetrating part of the mole and a visco-elastic slider, which represents the soil resistance. The model has been mathematically formulated and the equations of motion have been developed. A typical nonlinear dynamic analysis reveals a complex behaviour ranging from periodic to chaotic motion. It was found out that the maximum progression coincides with the end of the periodic regime.

Application of the harmonic balance method to ground moling machines operating in periodic regimes

Abstract A new system for ground moling has been patented by the University of Aberdeen and licensed world-wide. This new system is based on vibro-impact dynamics and offers significant advantages over existing systems in terms of penetrative capability and reduced soil disturbance. This paper describes current research into the mathematical modelling of the system. Periodic response is required to achieve the optimal penetrating conditions for the ground moling process, as this results in reduced soil penetration resistance. Therefore, there is a practical need for a robust and efficient methodology to calculate periodic responses for a wide range of operational parameters. Due to the structural complexity of a real vibro-impact moling system, the dynamic response of an idealised impact oscillator has been investigated in the first instance. This paper presents a detailed study of periodic responses of the impact oscillator under harmonic forcing using the alternating frequency-time harmonic balance method. Recommendations of how to effectively adapt the alternating frequency-time harmonic balance method for a stiff impacting system are given.

The influence of load on the frequency response of rock bolt anchorage

A research programme into both the static and dynamic performance of ground anchorage systems, with an emphasis on resin bonded rock bolts, started at the University of Aberdeen in the early 1980s. The work involved measurements on active construction sites which was underpinned by laboratory and computer modelling and led to the development of a new method for the non-destructive testing of anchorages. Part of the research programme was focused on assessing how changes in the load influence the dynamic responses of an anchorage. This in turn produced a new testing method able to determine the variation in frequency response of the anchorage with changes in load. The development of a lumped parameter model able to simulate the response of anchorages to changes in static load and an applied impulse load was a further step forward in the research programme. The achievements of the lumped parameter model revealed the head of the anchorage to be the most influential component of the anchorage system in determining dynamic response. The results obtained from the numerical model when laboratory anchorages were simulated are shown to be in agreement with the results obtained from the actual laboratory tests. Based on the successful results obtained to date regarding laboratory rock bolt anchorages, a further step was made in order to employ the numerical model for the first time to observe the influence of load on the frequency response of rock bolt anchorages installed in the field. This paper describes the application of the model to such field anchorages and highlights some of the detailed modelling aspects required to replicate real anchorage behaviour.

Using a Lumped Parameter Dynamic Model of a Rock Bolt to Produce Training Data for a Neural Network for Diagnosis of Real Data

Ground anchorage systems are used extensively throughout the world as supporting devices for civil engineering structures such as bridges and tunnels. The condition monitoring of ground anchorages is a new area of research, with the long term objective being a wholly automated or semi-automated condition monitoring system capable of repeatable and accurate diagnosis of faults and anchorage post-tension levels. The ground anchorage integrity testing (GRANIT) system operates by applying an impulse of known force by means of an impact device that is attached to the tendon of the anchorage. The vibration signals that arise from this impulse are complex in nature and require analysis to be undertaken in order to extract information from the vibrational response signatures that is relevant to the condition of the anchorage. Novel artificial intelligence techniques are used in order to learn the complicated relationship that exists between an anchorage and its response to an impulse. The system has a worldwide patent and is currently licensed commercially.A lumped parameter dynamic model has been developed which is capable of describing the general frequency relationship with increasing post-tension level as exhibited by the signals captured from real anchorages. The normal procedure with the system is to train a neural network on data that has been taken from an anchorage over a range of post-tension levels. Further data is needed in order to test the neural network. This process can be time consuming, and the lumped parameter dynamic model has the potential of producing data that could be used for training purposes, thereby reducing the amount of time needed on site, and reducing the overall cost of the systems operation.This paper presents data that has been produced by the lumped parameter dynamic model and compares it with data from a real anchorage. Noise is added to the results produced by the lumped parameter dynamic model in order to match more closely the experimental data. A neural network is trained on the data produced by the model, and the results of diagnosis of real data are presented. Problems are encountered with the diagnosis of the neural network with experimental data, and a new method for the training of the neural network is explored. The improved results of the neural network trained on data produced by the lumped parameter dynamic model to experimental data are shown. It is shown how the results from the lumped parameter dynamic model correspond well to the experimental results.

Condition Monitoring of Ground Anchorages using an Artificial Neural Network and Wavelet techniques

Ground anchorage systems in form of rock bolts are used extensively throughout the world as supporting devices for civil engineering constructions as varied as bridges, tunnels and dams. The condition monitoring of rock bolts is a new area of research, with the objective being a wholly automated or semi-automated condition monitoring system capable of repeatable and accurate diagnosis of faults and rockbolt post-tension levels. The automated system will comprise of a preprocessing phase incorporating wavelet techniques and an analysis phase comprising of a trained multi-layer perceptron neural network.

Use of neural networks in the condition monitoring of ground anchorages

The GRANIT system is a non-destructive integrity testing method for ground anchorages. It has won two major UK Awards--the Design Council Millennium Product Status in 1999 and the John Logie Baird Award in 1997. It makes use of novel artificial intelligence techniques in order to learn the complicated relationship that exists between an anchorage and its frequency response to an impulse. The GRANIT system has a worldwide patent and is currently licensed to AMEC plc.It is widely recognised that non-destructive testing methods for ground anchorages need to be developed as a high priority [A National Agenda for Long-term and Fundamental Research for Civil Engineering in the United Kingdom (1992)], with only between 1 and 5% of anchorages currently being monitored in service [British Standard Code of Practice for Ground Anchorages (8081) (1989)] using currently available techniques. The GRANIT system is a solution for this requirement, and this paper describes how the use of artificial intelligence techniques enabled, for the first time, the cross-anchorage diagnosis of ground anchorages, where data taken from one anchorage was used to train a neural network, which was then used to diagnose the condition of an adjacent anchorage.The results presented in this paper describe the training of a neural network on data taken from a bolt anchorage, and the diagnosis, using this neural network, of further test data taken from the same anchorage. Data taken from an adjacent anchorage of similar construction is also presented to the neural network, and the cross-anchorage diagnosis of the load level of the second anchorage is achieved.

Condition Monitoring of Ground Anchorages by Dynamic Impulses: GRANIT system

The GRANIT system operates by applying an impulse of known force by means of an impact device that is attached to the tendon of the anchorage. The vibration response signals resulting from this impulse are complex in nature and require analysis to be undertaken in order to extract information from the vibrational response signatures that is relevant to the condition of the anchorage. In the system, the complicated relationship that exists between characteristics of an anchorage and its response to an impulse is identified and learned by a novel artificial intelligence network based on artificial intelligence techniques.The results presented in this paper demonstrate the potential of the GRANIT system to diagnose the integrity of ground anchorages at a site near Stone, England, by using a trained neural network capable of diagnosing the post-tension level of the anchorage. This neural network was used for the diagnosis of load in a second ground anchorage adjacent to the original anchorage used for the training of the neural network. Further tests were taken with a different anchor head configuration of the anchorage and a different relationship between the signature response of the anchorage to an applied impulse and its post-tension level was found.Problems encountered during the diagnosis of this second set of test signatures by the trained neural network are investigated with the use of a lumped parameter dynamic model. This model is able to identify the parameters in the anchorage system that affect this change in response signature. The results from the investigation lead to a new form of classification for the installed anchorages, based on their anchor head configuration.Laboratory strand anchorage tests were undertaken in order to compare with and validate the results obtained from the field tests and the lumped parameter dynamic model.