Environmental simulation models have been playing a key role in civil and environmental engineering decision making processes for decades. The utility of an environmental model depends on how well the model is structured and calibrated. Model calibration is typically in an automated form where the simulation model is linked to a search mechanism (e.g., an optimization algorithm) such that the search mechanism iteratively generates many parameter sets (e.g., thousands of parameter sets) and evaluates them through running the model in an attempt to minimize differences between observed data and corresponding model outputs. The challenge rises when the environmental model is computationally intensive to run (with run-times of minutes to hours, for example) as then any automatic calibration attempt would impose a large computational burden. Such a challenge may make the model users accept sub-optimal solutions and not achieve the best model performance. The objective of this thesis is to develop innovative strategies to circumvent the computational burden associated with automatic calibration of computationally intensive environmental models. The first main contribution of this thesis is developing a strategy called “deterministic model preemption” which opportunistically evades unnecessary model evaluations in the course of a calibration experiment and can save a significant portion of the computational budget (even as much as 90% in some cases). Model preemption monitors the intermediate simulation results while the model is running and terminates (i.e., pre-empts) the simulation early if it recognizes that further running the model would not guide the search mechanism. This strategy is applicable to a range of automatic calibration algorithms (i.e., search mechanisms) and is deterministic in that it leads to exactly the same calibration results as when preemption is not applied. One other main contribution of this thesis is developing and utilizing the concept of “surrogate data” which is basically a reasonably small but representative proportion of a full set of calibration data. This concept is inspired by the existing surrogate modelling strategies where a surrogate model (also called a metamodel) is developed and utilized as a fast-to-run substitute of an original computationally intensive model. A framework is developed to efficiently calibrate hydrologic models to the full set of calibration data while running the original model only on surrogate data for the majority of candidate parameter sets, a strategy which leads to considerable computational saving. To this end, mapping relationships are developed to approximate the model performance on the full data based on the model performance on surrogate data. This framework can be applicable to the calibration of any environmental model where appropriate surrogate data and mapping relationships can be identified. As another main contribution, this thesis critically reviews and evaluates the large body of literature on surrogate modelling strategies from various disciplines as they are the most commonly used methods to relieve the computational burden associated with computationally intensive simulation models. To reliably evaluate these strategies, a comparative assessment and benchmarking framework is developed which presents a clear computational budget dependent definition for the success/failure of surrogate modelling strategies. Two large families of surrogate modelling strategies are critically scrutinized and evaluated: “response surface surrogate” modelling which involves statistical or data–driven function approximation techniques (e.g., kriging, radial basis functions, and neural networks) and “lower-fidelity physically-based surrogate” modelling strategies which develop and utilize simplified models of the original system (e.g., a groundwater model with a coarse mesh). This thesis raises fundamental concerns about response surface surrogate modelling and demonstrates that, although they might be less efficient, lower-fidelity physically-based surrogates are generally more reliable as they to-some-extent preserve the physics involved in the original model. Five different surface water and groundwater models are used across this thesis to test the performance of the developed strategies and elaborate the discussions. However, the strategies developed are typically simulation-model-independent and can be applied to the calibration of any computationally intensive simulation model that has the required characteristics. This thesis leaves the reader with a suite of strategies for efficient calibration of computationally intensive environmental models while providing some guidance on how to select, implement, and evaluate the appropriate strategy for a given environmental model calibration problem.

Environmental simulation models have been playing a key role in civil and environmental engineering decision making processes for decades. The utility of an environmental model depends on how well the model is structured and calibrated. Model calibration is typically in an automated form where the simulation model is linked to a search mechanism (e.g., an optimization algorithm) such that the search mechanism iteratively generates many parameter sets (e.g., thousands of parameter sets) and evaluates them through running the model in an attempt to minimize differences between observed data and corresponding model outputs. The challenge rises when the environmental model is computationally intensive to run (with run-times of minutes to hours, for example) as then any automatic calibration attempt would impose a large computational burden. Such a challenge may make the model users accept sub-optimal solutions and not achieve the best model performance. The objective of this thesis is to develop innovative strategies to circumvent the computational burden associated with automatic calibration of computationally intensive environmental models. The first main contribution of this thesis is developing a strategy called “deterministic model preemption” which opportunistically evades unnecessary model evaluations in the course of a calibration experiment and can save a significant portion of the computational budget (even as much as 90% in some cases). Model preemption monitors the intermediate simulation results while the model is running and terminates (i.e., pre-empts) the simulation early if it recognizes that further running the model would not guide the search mechanism. This strategy is applicable to a range of automatic calibration algorithms (i.e., search mechanisms) and is deterministic in that it leads to exactly the same calibration results as when preemption is not applied. One other main contribution of this thesis is developing and utilizing the concept of “surrogate data” which is basically a reasonably small but representative proportion of a full set of calibration data. This concept is inspired by the existing surrogate modelling strategies where a surrogate model (also called a metamodel) is developed and utilized as a fast-to-run substitute of an original computationally intensive model. A framework is developed to efficiently calibrate hydrologic models to the full set of calibration data while running the original model only on surrogate data for the majority of candidate parameter sets, a strategy which leads to considerable computational saving. To this end, mapping relationships are developed to approximate the model performance on the full data based on the model performance on surrogate data. This framework can be applicable to the calibration of any environmental model where appropriate surrogate data and mapping relationships can be identified. As another main contribution, this thesis critically reviews and evaluates the large body of literature on surrogate modelling strategies from various disciplines as they are the most commonly used methods to relieve the computational burden associated with computationally intensive simulation models. To reliably evaluate these strategies, a comparative assessment and benchmarking framework is developed which presents a clear computational budget dependent definition for the success/failure of surrogate modelling strategies. Two large families of surrogate modelling strategies are critically scrutinized and evaluated: “response surface surrogate” modelling which involves statistical or data–driven function approximation techniques (e.g., kriging, radial basis functions, and neural networks) and “lower-fidelity physically-based surrogate” modelling strategies which develop and utilize simplified models of the original system (e.g., a groundwater model with a coarse mesh). This thesis raises fundamental concerns about response surface surrogate modelling and demonstrates that, although they might be less efficient, lower-fidelity physically-based surrogates are generally more reliable as they to-some-extent preserve the physics involved in the original model. Five different surface water and groundwater models are used across this thesis to test the performance of the developed strategies and elaborate the discussions. However, the strategies developed are typically simulation-model-independent and can be applied to the calibration of any computationally intensive simulation model that has the required characteristics. This thesis leaves the reader with a suite of strategies for efficient calibration of computationally intensive environmental models while providing some guidance on how to select, implement, and evaluate the appropriate strategy for a given environmental model calibration problem.

Les pays industrialisés cherchent aujourd’hui à réduire leur consommation d'énergie et à utiliser des matières premières de substitution, notamment renouvelables dont le bois fait partie. Pour promouvoir son usage, le développement de méthodes favorisant son recours dans les systèmes constructifs pour la construction multiétage est nécessaire.La conception d’un bâtiment est multicritère. Des objectifs contradictoires sont à optimiser simultanément. Des solutions de compromis Pareto-optimaux sont par exemple recherchées entre l’atteinte des meilleures performances d’usage et la limitation de l’impact environnemental du bâtiment. Ces travaux portent ainsi sur le développement d’une méthode d’optimisation multiobjectif de systèmes constructifs bois adaptés au multiétage.Des objectifs de maximisation du confort vibratoire des planchers et de minimisation des besoins de chauffage, d’inconfort thermique, de potentiel de réchauffement climatique et d’énergie grise sont pris en compte. La méthode repose sur un algorithme d’optimisation multiobjectif par essaim particulaire capable de proposer un ensemble de solutions non-dominées constituant le front de Pareto. L’espace des solutions est contraint par des exigences réglementaires nécessaires à la sécurité de l’ouvrage. L’ensemble des fonctions-objectif est modélisé sous forme de fonctions analytiques. Les sorties d’intérêt du modèle de simulation thermique dynamique sont substituées par des métamodèles.La méthode développée est mise en oeuvre sur un cas d’étude. Les résultats obtenus offrent une grande diversité dans un panel de 20 000 solutions optimales. Ces résultats constituent un support de discussion entre les différents acteurs d’un projet de construction. / Industrialised countries are seeking to reduce their energy consumption and to use alternative raw materials, including renewables such as wood. To promote its use, multi-storey timber constructive systems need the development of new design methods.Building required a multicriteria design where conflicting objectives must be optimised simultaneously. Research solutions have to achieve the best Pareto-compromise between use performance and environmental impact of the building. This work aims to develop a multiobjective optimisation method of timber multi-storey building.The objectives of maximising floor vibration comfort and minimising heating needs, thermal discomfort, global warming potential and embodied energy are taken into account. A multi-objective particle swarm optimization algorithm is used to obtain a set of non-dominated solutions which is the Pareto front. The solution space is constrained by regulatory requirements necessary for the safety of the structure. All objective-functions are modelled as analytic functions. Dynamic thermal simulation model outputs are replaced by metamodels.The developed method is implemented on a case study. The results offer a great diversity in a panel of 20 000 optimal solutions. These results provide a basis for discussion between the different actors of a construction project.

Model-driven software development (MDSD) and component-based software development are both paradigms for reducing complexity and for increasing abstraction and reuse in software development. In this thesis, we aim at combining the advantages of each by introducing methods from component-based development into MDSD. In MDSD, all artefacts that describe a software system are regarded as models of the system and are treated as the central development artefacts. To obtain a system implementation from such models, they are transformed and integrated until implementation code can be generated from them. Models in MDSD can have very different forms: they can be documents, diagrams, or textual specifications defined in different modelling languages. Integrating these models of different formats and abstraction in a consistent way is a central challenge in MDSD. We propose to tackle this challenge by explicitly separating the tasks of defining model components and composing model components, which is also known as distinguishing programming-in-the-small and programming-in-the-large. That is, we promote a separation of models into models for modelling-in-the-small (models that are components) and models for modelling-in-the-large (models that describe compositions of model components). To perform such component-based modelling, we introduce two architectural styles for developing systems with component-based MDSD (CB-MDSD). For CB-MDSD, we require a universal composition technique that can handle models defined in arbitrary modelling languages. A technique that can handle arbitrary textual languages is universal invasive software composition for code fragment composition. We extend this technique to universal invasive software composition for graph fragments (U-ISC/Graph) which can handle arbitrary models, including graphical and textual ones, as components. Such components are called graph fragments, because we treat each model as a typed graph and support reuse of partial models. To put the composition technique into practice, we developed the tool Reuseware that implements U-ISC/Graph. The tool is based on the Eclipse Modelling Framework and can therefore be integrated into existing MDSD development environments based on the framework. To evaluate the applicability of CB-MDSD, we realised for each of our two architectural styles a model-driven architecture with Reuseware. The first style, which we name ModelSoC, is based on the component-based development paradigm of multi-dimensional separation of concerns. The architecture we realised with that style shows how a system that involves multiple modelling languages can be developed with CB-MDSD. The second style, which we name ModelHiC, is based on hierarchical composition. With this style, we developed abstraction and reuse support for a large modelling language for telecommunication networks that implements the Common Information Model industry standard.

Model-driven software development (MDSD) and component-based software development are both paradigms for reducing complexity and for increasing abstraction and reuse in software development. In this thesis, we aim at combining the advantages of each by introducing methods from component-based development into MDSD. In MDSD, all artefacts that describe a software system are regarded as models of the system and are treated as the central development artefacts. To obtain a system implementation from such models, they are transformed and integrated until implementation code can be generated from them. Models in MDSD can have very different forms: they can be documents, diagrams, or textual specifications defined in different modelling languages. Integrating these models of different formats and abstraction in a consistent way is a central challenge in MDSD. We propose to tackle this challenge by explicitly separating the tasks of defining model components and composing model components, which is also known as distinguishing programming-in-the-small and programming-in-the-large. That is, we promote a separation of models into models for modelling-in-the-small (models that are components) and models for modelling-in-the-large (models that describe compositions of model components). To perform such component-based modelling, we introduce two architectural styles for developing systems with component-based MDSD (CB-MDSD). For CB-MDSD, we require a universal composition technique that can handle models defined in arbitrary modelling languages. A technique that can handle arbitrary textual languages is universal invasive software composition for code fragment composition. We extend this technique to universal invasive software composition for graph fragments (U-ISC/Graph) which can handle arbitrary models, including graphical and textual ones, as components. Such components are called graph fragments, because we treat each model as a typed graph and support reuse of partial models. To put the composition technique into practice, we developed the tool Reuseware that implements U-ISC/Graph. The tool is based on the Eclipse Modelling Framework and can therefore be integrated into existing MDSD development environments based on the framework. To evaluate the applicability of CB-MDSD, we realised for each of our two architectural styles a model-driven architecture with Reuseware. The first style, which we name ModelSoC, is based on the component-based development paradigm of multi-dimensional separation of concerns. The architecture we realised with that style shows how a system that involves multiple modelling languages can be developed with CB-MDSD. The second style, which we name ModelHiC, is based on hierarchical composition. With this style, we developed abstraction and reuse support for a large modelling language for telecommunication networks that implements the Common Information Model industry standard.

The benefits of Model-Driven Software Development (MDSD) and Domain-Specific Languages (DSLs) wrt. efficiency and quality in software engineering increase the demand for custom languages and the need for efficient methods for language engineering. This motivated the introduction of language families that aim at further reducing the development costs and the maintenance effort for custom languages. The basic idea is to exploit the commonalities and provide means to enable systematic variation among a set of related languages. Current techniques and methodologies for language engineering are not prepared to deal with the particular challenges of language families. First, language engineering processes lack means for a systematic analysis, specification and management of variability as found in language families. Second, technical approaches for a modular specification and realisation of languages suffer from insufficient modularity properties. They lack means for information hiding, for explicit module interfaces, for loose coupling, and for flexible module integration. Our first contribution, Feature-Oriented Language Family Engineering (LFE), adapts methods from Software Product Line Engineering to the domain of language engineering. It extends Feature-Oriented Software Development to support metamodelling approaches used for language engineering and replaces state-of-the-art processes by a variability- and reuse-oriented LFE process. Feature-oriented techniques are used as means for systematic variability analysis, variability management, language variant specification, and the automatic derivation of custom language variants. Our second contribution, Integrative Role-Based Language Composition, extends existing metamodelling approaches with roles. Role models introduce enhanced modularity for object-oriented specifications like abstract syntax metamodels. We introduce a role-based language for the specification of language components, a role-based composition language, and an extensible composition system to evaluate role-based language composition programs. The composition system introduces integrative, grey-box composition techniques for language syntax and semantics that realise the statics and dynamics of role composition, respectively. To evaluate the introduced approaches and to show their applicability, we apply them in three major case studies. First, we use feature-oriented LFE to implement a language family for the ontology language OWL. Second, we employ role-based language composition to realise a component-based version of the language OCL. Third, we apply both approaches in combination for the development of SumUp, a family of languages for mathematical equations.

The procedure of joining flexible or nonrigid parts using applied loads is called compliant assembly, and it is widely used in automotive, aerospace, electronics, and appliance manufacturing. Uncontrolled assembly processes may produce geometric errors that can exceed design tolerances and induce an increment of elastic energy in the structure due to the accumulation of internal stresses. This condition might create unexpected deformations and residual stress distributions across the structure that compromise product functionality. This thesis presents a method based on nonlinear Finite Element Analysis (FEA), metamodelling, and optimization techniques to provide accurate and on-time shimming strategies to support the definition of optimum assembly strategies. An example of the method on a typical aerospace wing box structure is demonstrated in the present study. The delivered outputs intend to support the production line by anticipating the response of the structure under a specific assembly condition and presenting alternative assembly strategies that can be applied to address eventual predicted issues on product requirements. / Graduate

The benefits of Model-Driven Software Development (MDSD) and Domain-Specific Languages (DSLs) wrt. efficiency and quality in software engineering increase the demand for custom languages and the need for efficient methods for language engineering. This motivated the introduction of language families that aim at further reducing the development costs and the maintenance effort for custom languages. The basic idea is to exploit the commonalities and provide means to enable systematic variation among a set of related languages. Current techniques and methodologies for language engineering are not prepared to deal with the particular challenges of language families. First, language engineering processes lack means for a systematic analysis, specification and management of variability as found in language families. Second, technical approaches for a modular specification and realisation of languages suffer from insufficient modularity properties. They lack means for information hiding, for explicit module interfaces, for loose coupling, and for flexible module integration. Our first contribution, Feature-Oriented Language Family Engineering (LFE), adapts methods from Software Product Line Engineering to the domain of language engineering. It extends Feature-Oriented Software Development to support metamodelling approaches used for language engineering and replaces state-of-the-art processes by a variability- and reuse-oriented LFE process. Feature-oriented techniques are used as means for systematic variability analysis, variability management, language variant specification, and the automatic derivation of custom language variants. Our second contribution, Integrative Role-Based Language Composition, extends existing metamodelling approaches with roles. Role models introduce enhanced modularity for object-oriented specifications like abstract syntax metamodels. We introduce a role-based language for the specification of language components, a role-based composition language, and an extensible composition system to evaluate role-based language composition programs. The composition system introduces integrative, grey-box composition techniques for language syntax and semantics that realise the statics and dynamics of role composition, respectively. To evaluate the introduced approaches and to show their applicability, we apply them in three major case studies. First, we use feature-oriented LFE to implement a language family for the ontology language OWL. Second, we employ role-based language composition to realise a component-based version of the language OCL. Third, we apply both approaches in combination for the development of SumUp, a family of languages for mathematical equations.:1. Introduction 1.1. The Omnipresence of Language Families 1.2. Challenges for Language Family Engineering 1.3. Language Family Engineering with Features and Role-Based Composition 2. Review of Current Language Engineering 2.1. Language Engineering Processes 2.1.1. Analysis Phase 2.1.2. Design Phase 2.1.3. Implementation Phase 2.1.4. Applicability in Language Family Engineering 2.1.5. Requirements for an Enhanced LFE Process 2.2. Technical Approaches in Language Engineering 2.2.1. Specification of Abstract Syntax 2.2.2. Specification of Concrete Syntax 2.2.3. Specification of Semantics 2.2.4. Requirements for an Enhanced LFE Technique 3. Feature-Oriented Language Family Engineering 3.1. Foundations of Feature-Oriented SPLE 3.1.1. Introduction to SPLE 3.1.2. Feature-Oriented Software Development 3.2. Feature-Oriented Language Family Engineering 3.2.1. Variability and Variant Specification in LFE 3.2.2. Product-Line Realisation, Mapping and Variant Derivation for LFE 3.3. Case Study: Scalability in Ontology Specification, Evaluation and Application 3.3.1. Review of Evolution, Customisation and Combination in the OWL LanguageFamily 3.3.2. Application of Feature-Oriented Language Family Engineering for OWL 3.4. Discussion 3.4.1. Contributions 3.4.2. Related Work. 3.4.3. Conclusion 4. Integrative, Role-Based Composition for Language Family Engineering 4.1. Foundations of Role-Based Modelling. 4.1.1. Information Hiding and Interface Specification in Role Models 4.1.2. Loose Coupling and Flexible Integration in Role Composition 4.2. The LanGems Language Composition System 4.2.1. The Language Component Specification Language . 4.2.2. TheLanguageCompositionLanguage 4.2.3. TechniquesofLanguageComposition 4.3. Case Study: Component-based OCL 4.3.1. Role-Based OCL Modularisation 4.3.2. Role-Based OCL Composition 4.4. Discussion 4.4.1. Contributions 4.4.2. Related Work 4.4.3. Conclusion 5. LFE with Integrative, Role-Based Syntax and Semantics Composition 5.1. Integrating Features and Roles 5.2. SumUp Case Study 5.2.1. Motivation 5.2.2. Feature-Oriented Variability and Variant Specification 5.2.3. Role-Based Component Realisation 5.2.4. Feature-Oriented Variability and Variant Evolution 5.2.5. Model-driven Concrete Syntax Realisation 5.2.6. Model-driven Semantics Realisation 5.2.7. Role-Based Composition and Feature Mapping 5.2.8. Language Variant Derivation 5.3. Conclusion 6. Conclusion 6.1. Contributions 6.2. Outlook 6.2.1. Co-Evolution in Language Families 6.2.2. Role-Based Tool Integration. 6.2.3. Automatic Modularisation of Existing Language Families 6.2.4. Language Component Library Appendix A Appendix B Bibliography