Ian F. C. Smith

A direct stochastic algorithm for global search

This paper presents a new algorithm called probabilistic global search lausanne (PGSL). PGSL is founded on the assumption that optimal solutions can be identified through focusing search around sets of good solutions. Tests on benchmark problems having multi-parameter non-linear objective functions revealed that PGSL performs better than genetic algorithms and advanced algorithms for simulated annealing in 19 out of 23 cases studied. Furthermore as problem sizes increase, PGSL performs increasingly better than these other approaches. Empirical evidence of the convergence of PGSL is provided through its application to Lennard-Jones cluster optimisation problem. Finally, PGSL has already proved to be valuable for engineering tasks in areas of design, diagnosis and control.

Model-free data interpretation for continuous monitoring of complex structures

Civil engineering structures are difficult to model accurately and this challenge is compounded when structures are built in uncertain environments. As consequence, their real behavior is hard to predict; such difficulties have important effects on the reliability of damage detection. Such situations encourage the enhancement of traditional approximate structural assessments through in-service measurements and interpretation of monitoring data. While some proposals have recently been made, in general, no current methodology for detection of anomalous behavior from measurement data can be reliably applied to complex structures in practical situations. This paper presents two new methodologies for model-free data interpretation to identify and localize anomalous behavior in civil engineering structures. Two statistical methods (i) moving principal component analysis and (ii) moving correlation analysis have been demonstrated to be useful for damage detection during continuous static monitoring of civil structures. The algorithms are designed to learn characteristics of time series generated by sensor data during a period called the initialization phase where the structure is assumed to behave normally. This phase subsequently helps identify those behaviors which can be classified as anomalous. In this way the new methodologies can effectively identify anomalous behaviors without explicit (and costly) knowledge of structural characteristics such as geometry and models of behavior. The methodologies have been tested on numerically simulated elements with sensors at a range of damage severities. A comparative study with wavelet and other statistical analyses demonstrates superior performance for identifying the presence of damage.

CADRE: case-based geometric design

Abstract Case-based design (CBD) overcomes certain difficulties associated with traditional knowledge-based design systems. When novel designs are created by adapting cases, design integrity can be maintained through careful formulation of adaptation procedures. We describe a prototype design system called case adaptation by dimensionality reasoning (CADRE). Geometrical information is treated through simultaneous consideration of constraints expressed in terms of continuous variables. Complexity is controlled through problem specific (runtime) parameterizations and dimensionality reduction of relevant constraints. Run-time parameterizations also enable effective accommodation of. contextual parameters. Mixing mathematical constraints from different views of the design improves integration of diverse design criteria. Examples taken from building design are used to illustrate important aspects of the approach

Constraint-based support for negotiation in collaborative design

Reference IMAC-ARTICLE-2000-006doi:10.1016/S0954-1810(00)00020-0View record in Web of Science Record created on 2007-07-25, modified on 2016-08-08

Multimodel Structural Performance Monitoring

Measurements from load tests may lead to numerical models that better reflect structural behavior. This kind of system identification is not straightforward due to important uncertainties in measurement and models. Moreover, since system identification is an inverse engineering task, many models may fit measured behavior. Traditional model updating methods may not provide the correct behavioral model due to uncertainty and parameter compensation. In this paper, a multimodel approach that explicitly incorporates uncertainties and modeling assumptions is described. The approach samples thousands of models starting from a general parametrized finite-element model. The population of selected candidate models may be used to understand and predict behavior, thereby improving structural management decision making. This approach is applied to measurements from structural performance monitoring of the Langensand Bridge in Lucerne, Switzerland. Predictions from the set of candidate models are homogenous and show an average discrepancy of 4-7% from the displacement measurements. The tests demonstrate the applicability of the multimodel approach for the structural identification and performance monitoring of real structures. The multimodel approach reveals that the Langensand Bridge has a reserve capacity of 30% with respect to serviceability requirements.

Data mining techniques for improving the reliability of system identification

A system identification methodology that makes use of data mining techniques to improve the reliability of identification is presented in this paper. An important aspect of the methodology is the generation of a population of candidate models. Indications of the reliability of system identification are obtained through an examination of the characteristics of the population. Data mining techniques bring out model characteristics that are important. The methodology has been applied to several engineering systems.

Elasto-plastic modeling of wood bolted connections

A plasticity based constitutive compressive material model is proposed to model wood as elasto-plastic orthotropic according to the Hill yield criterion in regions of bi-axial compression. Linear elastic orthotropic material response is applied otherwise with maximum stresses taken as failure criteria. The model is implemented in the finite element code to carry out the analysis of bolted connections using ADINA software. Reasonable agreement is found between numerical simulations and experimental measurements of local and global deformation of one-bolt connection. The predicted failure modes are consistent with experimental observations.

Fatigue crack growth in a fillet welded joint

Abstract Existing theories for the growth of cracks at weld toes have proved difficult to verify because of a lack of experimental proof at short crack depths and slow growth rates. Arbitrary initial defect sizes have been employed in life calculations coupled with approximate two-dimensional stress analyses. In this study, the fatigue performance of a stress relieved fillet weld is determined by both theory and experiment. Crack growth results for shallow (less than 1 mm depth) elliptical cracks at weld toes are used to test an elastic expression for stress intensity using a correction factor from a three-dimensional stress analysis. No evidence of higher than expected growth rates, observed by others for very short cracks and cracks in notch plastic zones, is apparent. Integration of a growth law that includes the threshold stress intensity factor provides fatigue life predictions for various stress ratios and from experimentally measured defect depths. Needle peening the weld toe improves the fatigue life by retarding crack growth up to 1 mm below the weld toe.

Developing Intelligent Tensegrity Structures with Stochastic Search

Reference IMAC-CONF-2002-021doi:10.1016/S1474-0346(02)00003-4View record in Web of Science Record created on 2007-08-14, modified on 2016-08-08

Model falsification diagnosis and sensor placement for leak detection in pressurized pipe networks

Pressurized pipe networks used for fresh-water distribution can take advantage of recent advances in sensing technologies and data-interpretation to evaluate their performance. In this paper, a leak-detection and a sensor placement methodology are proposed based on leak-scenario falsification. The approach includes modeling and measurement uncertainties during the leak detection process. The performance of the methodology proposed is tested on a full-scale water distribution network using simulated data. Findings indicate that when monitoring the flow velocity for 14 pipes over the entire network (295 pipes) leaks are circumscribed within a few potential locations. The case-study shows that a good detectability is expected for leaks of 50L/min or more. A study of measurement configurations shows that smaller leak levels could also be detected if additional pipes are instrumented.