Dynamic centrifuge tests on shallow tunnel models in dry sand

In the framework of the Italian research project ReLUIS-DPC, a set of centrifuge tests were carried out at the Schofield Centre in Cambridge (UK) to investigate the seismic behaviour of tunnels. Four samples of dry sand were prepared at different densities, in which a small scale model of circular tunnel was inserted, instrumented with gauges measuring hoop and bending strains. Arrays of accelerometers in the soil and on the box allowed the amplification of ground motion to be evaluated; LVDTs measured the soil surface settlement. This paper describes the main results of this research, showing among others the evolution of the internal forces during the model earthquakes at significant locations along the tunnel lining. © 2010 Taylor & Francis Group, London.

Use of automatic sand pourers for loose sand models

The consistency of laboratory sand model preparation for physical testing is a fundamental criterion in representing identical geotechnical issues at prototype scale. This objective led to the development of robotic apparatus to eliminate the non-uniformity in manual pouring. Previous studies have shown consistent sand models with high relative density between 50 to 90% produced by the automatic moving-hopper sand pourer at the University of Cambridge, based primarily on a linear correlation to flow rate. However, in the case of loose samples, the influence of other parameters, particularly the drop height, becomes more apparent. In this paper, findings on the effect of flow rate and drop height are discussed in relation to the layer thickness and relative density of loose sand samples. Design charts are presented to illustrate their relationships. The effect of these factors on different sand types is also covered to extend the use of the equipment. © 2010 Taylor & Francis Group, London.

A comparison of two models for the vibration response of piled foundations to inertial and underground-railway-induced loadings

The vibration response of piled foundations due to ground-borne vibration produced by an underground railway is a largely-neglected area in the field of structural dynamics. However, this continues to be an important aspect of research as it is expected that the presence of piled foundations can have a significant influence on the propagation and transmission of the wavefield produced by the underground railway. This paper presents a comparison of two methods that can be employed in calculating the vibration response of a piled foundation: an efficient semi-analytical model, and a Boundary Element model. The semi-analytical model uses a column or an Euler beam to model the pile, and the soil is modelled as a linear, elastic continuum that has the geometry of a thick-walled cylinder with an infinite outer radius and an inner radius equal to the radius of the pile. The boundary element model uses a constant-element BEM formulation for the halfspace, and a rectangular discretisation of the circular pile-soil interface. The piles are modelled as Timoshenko beams. Pile-soil-pile interactions are inherently accounted for in the BEM equations, whereas in the semi-analytical model these are quantified using the superposition of interaction factors. Both models use the method of joining subsystems to incorporate the incident wavefield generated by the underground railway into the pile model. Results are computed for a single pile subject to an inertial loading, pile-soil-pile interactions, and a pile group subjected to excitation from an underground railway. The two models are compared in terms of accuracy, computation time, versatility and applicability, and guidelines for future vibration prediction models involving piled foundations are proposed.

State estimation schemes for independent component coupled hidden Markov models

Conventional Hidden Markov models generally consist of a Markov chain observed through a linear map corrupted by additive noise. This general class of model has enjoyed a huge and diverse range of applications, for example, speech processing, biomedical signal processing and more recently quantitative finance. However, a lesser known extension of this general class of model is the so-called Factorial Hidden Markov Model (FHMM). FHMMs also have diverse applications, notably in machine learning, artificial intelligence and speech recognition [13, 17]. FHMMs extend the usual class of HMMs, by supposing the partially observed state process is a finite collection of distinct Markov chains, either statistically independent or dependent. There is also considerable current activity in applying collections of partially observed Markov chains to complex action recognition problems, see, for example, [6]. In this article we consider the Maximum Likelihood (ML) parameter estimation problem for FHMMs. Much of the extant literature concerning this problem presents parameter estimation schemes based on full data log-likelihood EM algorithms. This approach can be slow to converge and often imposes heavy demands on computer memory. The latter point is particularly relevant for the class of FHMMs where state space dimensions are relatively large. The contribution in this article is to develop new recursive formulae for a filter-based EM algorithm that can be implemented online. Our new formulae are equivalent ML estimators, however, these formulae are purely recursive and so, significantly reduce numerical complexity and memory requirements. A computer simulation is included to demonstrate the performance of our results. © Taylor & Francis Group, LLC.

Bayesian parameter identification for vibro-acoustic models with nonparametric uncertainty

Vibration and acoustic analysis at higher frequencies faces two challenges: computing the response without using an excessive number of degrees of freedom, and quantifying its uncertainty due to small spatial variations in geometry, material properties and boundary conditions. Efficient models make use of the observation that when the response of a decoupled vibro-acoustic subsystem is sufficiently sensitive to uncertainty in such spatial variations, the local statistics of its natural frequencies and mode shapes saturate to universal probability distributions. This holds irrespective of the causes that underly these spatial variations and thus leads to a nonparametric description of uncertainty. This work deals with the identification of uncertain parameters in such models by using experimental data. One of the difficulties is that both experimental errors and modeling errors, due to the nonparametric uncertainty that is inherent to the model type, are present. This is tackled by employing a Bayesian inference strategy. The prior probability distribution of the uncertain parameters is constructed using the maximum entropy principle. The likelihood function that is subsequently computed takes the experimental information, the experimental errors and the modeling errors into account. The posterior probability distribution, which is computed with the Markov Chain Monte Carlo method, provides a full uncertainty quantification of the identified parameters, and indicates how well their uncertainty is reduced, with respect to the prior information, by the experimental data. © 2013 Taylor & Francis Group, London.

gcs-ma: a group communication system for mobile agents

Reliable messaging is a key component necessary for mobile agent systems. Current researches focus on reliable one-to-one message delivery to mobile agents. But how to implement a group communication system for mobile agents remains an open issue, which is a powerful block that facilitates the development of fault-tolerant mobile agent systems. In this paper, we propose a group communication system for mobile agents (GCS-MA), which includes totally ordered multicast and membership management functions. We divide a group of mobile agents into several agent clusters,and each agent cluster consists of all mobile agents residing in the same sub-network and is managed by a special module, named coordinator. Then, all coordinators form a ring-based overlay for interchanging messages between clusters. We present a token-based algorithm, an intra-cluster messaging algorithm and an inter-cluster migration algorithm to achieve atomicity and total ordering properties of multicast messages, by building a membership protocol on top of the clustering and failure detection mechanisms. Performance issues of the proposed system have been analysed through simulations. We also describe the application of the proposed system in the context of the service cooperation middleware (SCM) project.

A decentralized batch-based group key management potocol for mobile Internet of Thngs (DBGK).

It is anticipated that constrained devices in the Internet of Things (IoT) will often operate in groups to achieve collective monitoring or management tasks. For sensitive and mission-critical sensing tasks, securing multicast applications is therefore highly desirable. To secure group communications, several group key management protocols have been introduced. However, the majority of the proposed solutions are not adapted to the IoT and its strong processing, storage, and energy constraints. In this context, we introduce a novel decentralized and batch-based group key management protocol to secure multicast communications. Our protocol is simple and it reduces the rekeying overhead triggered by membership changes in dynamic and mobile groups and guarantees both backward and forward secrecy. To assess our protocol, we conduct a detailed analysis with respect to its communcation and storage costs. This analysis is validated through simulation to highlight energy gains. The obtained results show that our protocol outperforms its peers with respect to keying overhead and the mobility of members.

On Models, Modelling and the Distinctive Nature of Model-Based Reasoning

M.H. Lee, On Models, Modelling and the Distinctive Nature of Model-Based Reasoning, AI Communications, 12 (3), pp127-137.1999.

Qualitative Circuit Models in Failure Analysis Reasoning

Lee M.H., Qualitative Circuit Models in Failure Analysis Reasoning, AI Journal. vol 111, pp239-276.1999.

Automating Mechanical FMEA Using Functional Models

Hughes, N., Chou E., Price, C. J. Lee M. H.(1999). Automating Mechanical FMEA Using Functional Models, Proceedings 12th Int. Florida AI Research Soc. Conf. (FLAIRS-99), AAAI Press, May 1999, pp. 394-398.

Learning Qualitative Metabolic Models

Coghill, G. M., Garrett, S. M. and King, R. D. (2004) Learning Qualitative Metabolic Models. European Conference on Artificial Intelligence (ECAI'04)

Learning Qualitative Models in the Presence of Noise

G. M. Coghill, S. M. Garrett and R. D. King (2002), Learning Qualitative Models in the Presence of Noise, QR'02 Workshop on Qualitative Reasoning

Fuzzy interpolative reasoning via scale and move transformation

Z. Huang and Q. Shen. Fuzzy interpolative reasoning via scale and move transformation. IEEE Transactions on Fuzzy Systems, 14(2):340-359.

Modifying weighted fuzzy subsethood-based rule models with fuzzy quantifiers

K. Rasmani and Q. Shen. Modifying weighted fuzzy subsethood-based rule models with fuzzy quantifiers. Proceedings of the 13th International Conference on Fuzzy Systems, pages 1679-1684, 2004

Fuzzy Interpolation and Extrapolation: A Practical Approach

Z. Huang and Q. Shen. Fuzzy interpolative and extrapolative reasoning: a practical approach. IEEE Transactions on Fuzzy Systems, 16(1):13-28, 2008.