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This paper presents a model transformation between System Dynamics Model (SDM) and Artificial Neural Network (ANN) to aid model construction and policy design. We first point out a similarity between a System Dynamics Model (SDM) and an artificial neural network, in which both store knowledge majorly in the structure (or linkages) of a model. Then, we design a method that can map a SDM to a special design Partial Recurrent Network (PRN), and prove in mathematics that they two operate under the same numerical propagation constraints. With the established foundation, we then showed that the SDM-PRN transformation could aid SDM construction in the following way: (1) start from an initial skeleton of a PRN model (mapping from an initial SDM), (2) incarnate its structure by learning and (3) convert it back to a corresponding SDM. This approach integrates the capability of neural network learning with a traditional process, which thus makes model construction more systematic and much easier for common people. In the same philosophy, the SDM-PRN transformation could also aid SD policy design. Since any PRN can learn some structures from a historical time series pattern, it can also learn a better structure from a better pattern set by designer. We have investigated the effectiveness and usefulness of two application of the SDM-PRN transformation described above and the results are satisfactory.

Mega construction projects are complex undertakings. The complexity is increased during a construction boom in a city such as Dubai where more than 80% of material and labour resources are imported. The complexities inherent in mega construction projects include uncertainty and interdependencies, which affect project management performance at the operational level where most of the problems occur. Understanding these processes, how they interrelate and how to overcome the complexities is crucial for increasing the chances of project management success. This research contributed to construction project management theory and practice by using a highly inductive exploratory qualitative approach for capturing formal and informal processes in mega construction projects in Dubai. A dynamic model depicting the complementary formal and informal processes is developed showing formal and informal processes and how they interrelate to reduce complexity. Guidelines on how and when to use specific processes are provided as a framework for the model. This framework may be used to help develop communication processes in future complex projects to increase chances of project management success. The research was conducted by exploring two case study projects fitting the characteristics of mega construction projects. Both case study projects were of values above 1.5 Billion US Dollars and were similar in scope with elements including amusement parks, retail areas, hotels, extensive infrastructure and large landscape areas. Both case study projects were of an experimental character, considered an engineering achievement, complex, and thereby, difficult to control. The research data were collected through semi-structured interviews with 83 project staff from both case study projects. Rich picture diagrams of the organization and processes were made from data collected during the interviews and case study project documents. The analysis was performed in three phases where the results from each phase were built upon cumulatively. The first phase examined what the most crucial critical success factors (CSFs) were in the case study projects. These were revealed to be communication, top management support and effective change management. In the second phase each CSF was analyzed where it was found it that the majority of important communication modes were informal and were was most effective in reducing complexity. Furthermore, it was found that the characteristics of the client were crucial in both case study projects. The third phase involved development of the dynamic hybrid model from the rich picture diagrams created showing the interplay between formal and informal processes. It was found that informal processes are ad-hoc and chaotic in nature and controlled by individuals in the project. Development of guidelines for the positive use of informal processes was required. From the analysis it was found that the characteristics of client staff and individuals in the case study projects provided the main guidelines for effective use of the hybrid model. The research provides insights into the formal and informal processes and the reduction and management of complexity inherent in mega construction projects. Current research in managing complexity is from the perspective of the formal process where the informal processes are neglected, despite research and practice indicating their importance in construction projects. The research presents a systemic dynamic model of the processes for mega construction projects revealing that formal and informal processes are required and are complementary. Guidelines for the successful use of the model provide a framework for its application in practice. This shows how it can be used to inform understanding of processes in the context of the research. The model and guidelines were validated via discussions with practitioners experienced in construction of mega projects. Future work should expand the findings to applications of the model in practice for management of complex projects. The model may be used by practitioners to act as a guide in how to manage construction processes to consciously overcome the inherent complexities to achieve project management objectives. This tool may also be used to inform clients of the inherent complexities and the level of informality required to overcome them. IT/IS practitioners may use the model and framework to understand the complexities in construction, and thereby, develop systems that are practically applied in construction projects. Researchers in the field may build upon the model and findings to increase knowledge regarding construction processes, particularly informal processes.

Although deep saline aquifers are found in all sedimentary basins and provide very large storage capacities, a little is known about them because they are rarely a target for the exploration. Furthermore, nearly all the experiments and simulations made for CO2 sequestration in deep saline aquifers are related to the sandstone formations. The aim of this study is to create a predictive model to estimate the CO2 storage capacity of the deep saline carbonate aquifers since a little is known about them. To create a predictive model, the variables which affect the CO2 storage capacity and their ranges are determined from published literature data. They are rock properties (porosity, permeability, vertical to horizontal permeability ratio), fluid properties (irreducible water saturation, gas permeability end point, Corey water and gas coefficients), reaction properties (forward and backward reaction rates) and reservoir properties (depth, pressure gradient, temperature gradient, formation dip angle, salinity), diffusion coefficient and Kozeny-Carman Coefficient. Other parameters such as pore volume compressibility and density of brine are calculated from correlations found in literature. To cover all possibilities, Latin Hypercube Space Filling Design is used to construct 100 simulation cases and CMG STARS is used for simulation runs. By using least squares method, a linear correlation is found to calculate CO2 storage capacity of the deep saline carbonate aquifers with a correlation coefficient 0.81 by using variables found from literature and simulation results. Numerical dispersion effects have been considered by increasing the grid dimensions. It has been found that correlation coefficient decreased to 0.77 when the grid size was increased from 250 ft to 750 ft. The sensitivity analysis shows that the most important parameter that affects CO2 storage capacity is depth since the pressure difference between formation pressure and fracture pressure increases with depth. Also, CO2 storage mechanisms are investigated at the end of 300 years of simulation. Most of the gas (up to 90%) injected into formation dissolves into the formation water and negligible amount of CO2 reacts with carbonate. This result is consistent with sensitivity analysis results since the variables affecting the solubility of CO2 in brine have greater affect on storage capacity of aquifers. Dimensionless linear and nonlinear predictive models are constructed to estimate the CO2 storage capacity of all deep saline carbonate aquifers and it is found that the best dimensionless predictive model is linear one independent of bulk volume of the aquifer.

<p>It is often beneficial for an autonomous agent that operates in a complex environment to make use of different types of mathematical models to keep track of unobservable parts of the world or to perform prediction, planning and other types of reasoning. Since a model is always a simplification of something else, there always exists a tradeoff between the model’s accuracy and feasibility when it is used within a certain application due to the limited available computational resources. Currently, this tradeoff is to a large extent balanced by humans for model construction in general and for autonomous agents in particular. This thesis investigates different solutions where such agents are more responsible for balancing the tradeoff for models themselves in the context of interleaved task planning and plan execution. The necessary components for an autonomous agent that performs its abstractions and constructs planning models dynamically during task planning and execution are investigated and a method called DARE is developed that is a template for handling the possible situations that can occur such as the rise of unsuitable abstractions and need for dynamic construction of abstraction levels. Implementations of DARE are presented in two case studies where both a fully and partially observable stochastic domain are used, motivated by research with Unmanned Aircraft Systems. The case studies also demonstrate possible ways to perform dynamic abstraction and problem model construction in practice.</p> / Report code: LiU-Tek-Lic-2008:21.

The construction industry has inherent problems due to its structure and fragmentation. Its poor performance has been challenged by its client base and it has been forced to seek ways to deliver improved performance. This project was initiated as a response to this challenge and represents one organisation's attempt to deliver improvements. This organisation provides both design and construction solutions, offering ‘total life of asset support' from business consultancy through to decommissioning, in a neutral contractual environment. Initial investigations of the integration of design and construction and of the use of the EFQM Excellence Model concluded that a holistic view of the organisation's performance was needed. Most organisations use traditional, easily quantifiable measures, such as time and cost, whilst neglecting the softer cultural issues, as a way of assessing overall business performance. This prompted further research into the use of performance measurement and also a review of the culture that existed within the organisation. It became clear that many performance initiatives failed because of the lack of ‘Change Action driven by Results (CAR)'. The failure to initiate change or implement action based on the results achieved, means that performance measures are not being integrated into the management systems of the organisations. Based on the conclusions of this work, this project has developed and implemented the Integrated Business Improvement System (IBIS) within the primary case study unit and also two other organisations, all of which are part of AMEC Plc. The project details the barriers that were experienced during the development and implementation of the system and concludes that it is the human component that is critical for the successful implementation and use of any improvement system. The findings of this work have been presented in five peer- reviewed papers.

Safety-critical systems, especially those found in avionics and automotive industries, rely on machine arithmetic to perform their tasks: integer arithmetic, fixed-point arithmetic or floating-point arithmetic (FPA). Machine arithmetic exhibits subtle differences in behavior compared to the ideal mathematical arithmetic, due to fixed-size representation in memory. Failure of safety-critical systems is unacceptable, due to high-stakes involving human lives or huge amounts of money, time and effort. By formally proving properties of systems, we can be assured that they meet safety requirements. However, to prove such properties it is necessary to reason about machine arithmetic. SMT techniques for machine arithmetic are lacking scalability. This thesis presents approaches that augment or complement existing SMT techniques for machine arithmetic. In this thesis, we explore approximations as a means of augmenting existing decision procedures. A general approximation refinement framework is presented, along with its implementation called UppSAT. The framework solves a sequence of approximations. Initially very crude, these approximations are fairly easy to solve. Results of solving approximate constraints are used to either reconstruct a solution of original constraints, obtain a proof of unsatisfiability or to refine the approximation. The framework preserves soundness, completeness, and termination of the underlying decision procedure, guaranteeing that eventually, either a solution is found or a proof that solution does not exist. We evaluate the impact of approximations implemented in the UppSAT framework on the state-of-the-art in SMT for floating-point arithmetic. A novel method to reason about the theory of fixed-width bit-vectors called mcBV is presented. It is an instantiation of the model constructing satisfiability calculus, mcSAT, and uses a new lazy representation of bit-vectors that allows both bit- and word-level reasoning. It uses a greedy explanation generalization mechanism capable of more general learning compared to traditional approaches. Evaluation of mcBV shows that it can outperform bit-blasting on several classes of problems.

It is often beneficial for an autonomous agent that operates in a complex environment to make use of different types of mathematical models to keep track of unobservable parts of the world or to perform prediction, planning and other types of reasoning. Since a model is always a simplification of something else, there always exists a tradeoff between the model’s accuracy and feasibility when it is used within a certain application due to the limited available computational resources. Currently, this tradeoff is to a large extent balanced by humans for model construction in general and for autonomous agents in particular. This thesis investigates different solutions where such agents are more responsible for balancing the tradeoff for models themselves in the context of interleaved task planning and plan execution. The necessary components for an autonomous agent that performs its abstractions and constructs planning models dynamically during task planning and execution are investigated and a method called DARE is developed that is a template for handling the possible situations that can occur such as the rise of unsuitable abstractions and need for dynamic construction of abstraction levels. Implementations of DARE are presented in two case studies where both a fully and partially observable stochastic domain are used, motivated by research with Unmanned Aircraft Systems. The case studies also demonstrate possible ways to perform dynamic abstraction and problem model construction in practice. / <p>Report code: LiU-Tek-Lic-2008:21.</p>

Die Bauindustrie ist im Wandel. Gebäude sollen zukünftig nicht nur physisch auf dem Baufeld entstehen, sondern auch detailgetreu inklusive sämtlicher Informationen in einem BIM-Modell: als digitaler Zwilling. Auch das Unternehmen GOLDBECK möchte diesen Weg gehen und hat sich mit seiner Strategie „BIM@GOLDBECK 2025“ eigene Ziele gesteckt. Um diese Ziele erfüllen zu können, steht die Abteilung Konstruktion für Betonfertigteile vor mehreren Herausforderungen. Im Zuge dieser Arbeit wird nach einem ersten Überblick über den aktuellen Stand der Technik zum Thema BIM ein Einblick in die genannte Strategie des Unternehmens sowie dessen bisherige Umsetzung in Planung, Ausführung und Forschung gegeben. Anschließend werden die aktuelle Arbeitsweise in der Konstruktion für Betonfertigteile und deren Ziele für eine künftige Arbeitswei-se vorgestellt. Es zeigt sich, dass es erforderlich ist, eine BIM-basierte Konstruktionssoftware ein-zuführen. Aus dieser Erkenntnis heraus wird eine Marktanalyse der auf dem Markt verfügbaren, potentiellen Softwares durchgeführt. Die zwei Softwares mit dem aus Unternehmenssicht höchs-ten Potential werden anschließend an einem gewählten Bauteil verprobt. Ein Vergleich der Ergeb-nisse aus den beiden Bauteiltests mit den bisher eingesetzten Softwarelösungen führt zu einer Entscheidungsempfehlung. Auf der Grundlage neuer technischer Voraussetzungen kann die Ar-beitsweise innerhalb der Konstruktion angepasst werden. Dadurch ergeben sich enorme zu-kunftsträchtige Chancen für das Unternehmen.:Motivation Ausgangslage Untersuchungen Willkommen in der Zukunft!

Hardware description languages(HDLs) are frequently used to construct performance models to represent systems early in the design process. This research study has resulted in the development of a methodology to construct VHDL performance models which will help to significantly reduce the time from an initial conception to a working design. To further reduce development time, reuse of existing structural primitives is emphasized. Typical models of multi-processor architectures are very large and complex. Validation of theses models is difficult and time consuming. This thesis also develops a methodology for model validation. A seventeen processor raceway architecture, that was developed as a part of the ongoing RASSP(Rapid Prototyping of Application Specific Signal Processors) project, is used as a template to illustrate the new methodologies of performance model construction and model validation. The design consists of seventeen processors interconnected by multiple crossbar switches. Two software algorithms were mapped onto the architecture: a Synthetic Aperture Radar(SAR) Range Processing Algorithm and a SAR Multiswath Processing Algorithm. The methodologies developed in this thesis will considerably reduce the amount of time needed to construct and validate performance models of complex multi-processor architectures. / Master of Science