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Thesis (PhD)--University of Stellenbosch, 2002. / ENGLISH ABSTRACT: In this dissertation the traditional model-centred (MC)design approach for the development of software in the civil engineering field is compared to a newly developed application-centred (AC)design approach. In the MC design software models play the central role. A software model maps part of the world, for example its visualization or analysis onto the memory space of the computer. Characteristic of the MC design is that the identifiers of objects are unique and persistent only within the name scope of a model, and that classes which define the objects are components of the model. In the AC design all objects of the engineering task are collected in an application. The identifiers of the objects are unique and persistent within the name scope of the application and classes are no longer components of a model, but components of the software platform. This means that an object can be a part of several models. It is investigated whether the demands on the information and communication in modern civil engineering processes can be satisfied using the MC design approach. The investigation is based on the evaluation of existing software for the analysis and design of a sewer reticulation system of realistic dimensions and complexity. Structural, quantitative, as well as engineering complexity criteria are used to evaluate the design. For the evaluation of the quantitative criteria, in addition to the actual Duration of Execution, a User Interaction Count, the Persistent Data Size, and a Basic Instruction Count based on a source code complexity analysis, are introduced. The analysis of the MCdesign shows that the solution of an engineering task requires several models. The interaction between the models proves to be complicated and inflexible due to the limitation of object identifier scope: The engineer is restricted to the concepts of the software developer, who must provide static bridges between models in the form of data files or software transformers. The concept of the ACdesign approach is then presented and implemented in a new software application written in Java. This application is also extended for the distributed computing scenario. Newbasic classes are defined to manage the static and dynamic behaviour of objects, and to ensure the consistent and persistent state of objects in the application. The same structural and quantitative analyses are performed using the same test data sets as for the MCapplication. It is shown that the AC design approach is superior to the MC design approach with respect to structural, quantitative and engineering complexity .criteria. With respect to the design structure the limitation of object identifier scope, and thus the requirement for bridges between models, falls away, which is in particular of value for the distributed computing scenario. Although the new object management routines introduce an overhead in the duration of execution for the AC design compared to a hypothetical MC design with only one model and no software bridges, the advantages of the design structure outweigh this potential disadvantage. / AFRIKAANSE OPSOMMING: In hierdie proefskrif word die tradisionele modelgesentreerde (MC)ontwerpbenadering vir die ontwikkeling van sagteware vir die siviele ingenieursveld vergelyk met 'n nuut ontwikkelde applikasiegesentreerde (AC) ontwerpbenadering. In die MContwerp speel sagtewaremodelle 'n sentrale rol. 'n Sagtewaremodel beeld 'n deel van die wêreld, byvoorbeeld die visualisering of analise op die geheueruimte van die rekenaar af. Eienskappe van die MContwerp is dat die identifiseerders van objekte slegs binne die naamruimte van 'n model uniek en persistent is, en dat klasse wat die objekte definieer komponente van die model is. In die AC ontwerp is alle objekte van die ingenieurstaak saamgevat in 'n applikasie. Die identifisieerders van die objekte is uniek en persistent binne die naamruimte van die applikasie en klasse is nie meer komponente van die model nie, maar komponente van die sagtewareplatform. Dit beteken dat 'n objek deel van 'n aantal modelle kan vorm. Dit word ondersoek of daar by die MC ontwerpbenadering aan die vereistes wat by moderne siviele ingenieursprosesse ten opsigte van inligting en kommunikasie gestel word, voldoen kan word. Die ondersoek is gebaseer op die evaluering van bestaande sagteware vir die analise en ontwerp van 'n rioolversamelingstelsel met realistiese dimensies en kompleksiteit. Strukturele, kwantitatiewe, sowel as ingenieurskompleksiteitskriteria word gebruik om die ontwerp te evalueer. Vir die evaluering van die kwantitatiewe kriteria word addisioneel tot die uitvoerduurte 'n gebruikersinteraksie-telling, die persistente datagrootte, en 'n basiese instruksietelling gebaseer op 'n bronkode kompleksiteitsanalise , ingevoer. Die analise van die MC ontwerp toon dat die oplossing van ingenieurstake 'n aantal modelle benodig. Die interaksie tussen die modelle bewys dat dit kompleks en onbuigsaam is, as gevolg van die beperking op objekidentifiseerderruimte: Die ingenieur is beperk tot die konsepte van die sagteware ontwikkelaar wat statiese brue tussen modelle in die vorm van lêers of sagteware transformators moet verskaf. Die AC ontwerpbenadering word dan voorgestel en geïmplementeer in 'n nuwe sagteware-applikasie, geskryf in Java. Die applikasie word ook uitgebrei vir die verdeelde bewerking in die rekenaarnetwerk. Nuwe basisklasse word gedefinieer om die statiese en dinamiese gedrag van objekte te bestuur, en om die konsistente en persistente status van objekte in die applikasie te verseker. Dieselfde strukturele en kwantitatiewe analises word uitgevoer met dieselfde toetsdatastelle soos vir die MC ontwerp. Daar word getoon dat die AC ontwerpbenadering die MC ontwerpbenadering oortref met betrekking tot die strukturele, kwantitatiewe en ingenieurskompleksiteitskriteria. Met betrekking tot die ontwerpstruktuur val die beperking van die objek-identfiseerderruimte en dus die vereiste van brue tussen modelle weg, wat besonder voordelig is vir die verdeelde bewerking in die rekenaarnetwerk. Alhoewel die nuwe objekbestuurroetines in die AC ontwerp in vergelyking met 'n hipotetiese MC ontwerp, wat slegs een model en geen sagteware brue bevat, langer uitvoerduurtes tot gevolg het, is die voordele van die ontwerpstruktuur groter as die potensiële nadele.

Many methods have been developed and studied to detect damage through the change of dynamic response of a structure. Due to its capability to recognize pattern and to correlate non-linear and non-unique problem, Artificial Neural Networks (ANN) have received increasing attention for use in detecting damage in structures based on vibration modal parameters. Most successful works reported in the application of ANN for damage detection are limited to numerical examples and small controlled experimental examples only. This is because of the two main constraints for its practical application in detecting damage in real structures. They are: 1) the inevitable existence of uncertainties in vibration measurement data and finite element modeling of the structure, which may lead to erroneous prediction of structural conditions; and 2) enormous computational effort required to reliably train an ANN model when it involves structures with many degrees of freedom. Therefore, most applications of ANN in damage detection are limited to structure systems with a small number of degrees of freedom and quite significant damage levels. In this thesis, a probabilistic ANN model is proposed to include into consideration the uncertainties in finite element model and measured data. Rossenblueth's point estimate method is used to reduce the calculations in training and testing the probabilistic ANN model. The accuracy of the probabilistic model is verified by Monte Carlo simulations. Using the probabilistic ANN model, the statistics of the stiffness parameters can be predicted which are used to calculate the probability of damage existence (PDE) in each structural member. The reliability and efficiency of this method is demonstrated using both numerical and experimental examples. In addition, a parametric study is carried out to investigate the sensitivity of the proposed method to different damage levels and to different uncertainty levels. As an ANN model requires enormous computational effort in training the ANN model when the number of degrees of freedom is relatively large, a substructuring approach employing multi-stage ANN is proposed to tackle the problem. Through this method, a structure is divided to several substructures and each substructure is assessed separately with independently trained ANN model for the substructure. Once the damaged substructures are identified, second-stage ANN models are trained for these substructures to identify the damage locations and severities of the structural ii element in the substructures. Both the numerical and experimental examples are used to demonstrate the probabilistic multi-stage ANN methods. It is found that this substructuring ANN approach greatly reduces the computational effort while increasing the damage detectability because fine element mesh can be used. It is also found that the probabilistic model gives better damage identification than the deterministic approach. A sensitivity analysis is also conducted to investigate the effect of substructure size, support condition and different uncertainty levels on the damage detectability of the proposed method. The results demonstrated that the detectibility level of the proposed method is independent of the structure type, but dependent on the boundary condition, substructure size and uncertainty level.

"February 2003." Corrigenda for, inserted at back Includes bibliographical references (leaves 475-524 ) A methodology is formulated for the successful design and implementation of artificial neural networks (ANN) models for water resources applications. Attention is paid to each of the steps that should be followed in order to develop an optimal ANN model; including when ANNs should be used in preference to more conventional statistical models; dividing the available data into subsets for modelling purposes; deciding on a suitable data transformation; determination of significant model inputs; choice of network type and architecture; selection of an appropriate performance measure; training (optimisation) of the networks weights; and, deployment of the optimised ANN model in an operational environment. The developed methodology is successfully applied to two water resorces case studies; the forecasting of salinity in the River Murray at Murray Bridge, South Australia; and the the forecasting of cyanobacteria (Anabaena spp.) in the River Murray at Morgan, South Australia.

"February 2003." / Corrigenda for, inserted at back / Includes bibliographical references (leaves 475-524 ) / xxx, 524 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / A methodology is formulated for the successful design and implementation of artificial neural networks (ANN) models for water resources applications. Attention is paid to each of the steps that should be followed in order to develop an optimal ANN model; including when ANNs should be used in preference to more conventional statistical models; dividing the available data into subsets for modelling purposes; deciding on a suitable data transformation; determination of significant model inputs; choice of network type and architecture; selection of an appropriate performance measure; training (optimisation) of the networks weights; and, deployment of the optimised ANN model in an operational environment. The developed methodology is successfully applied to two water resorces case studies; the forecasting of salinity in the River Murray at Murray Bridge, South Australia; and the the forecasting of cyanobacteria (Anabaena spp.) in the River Murray at Morgan, South Australia. / Thesis (Ph.D.)--University of Adelaide, School of Civil and Environmental Engineering, 2003