The Experts below are selected from a list of 623940 Experts worldwide ranked by ideXlab platform
Simon A Austin - One of the best experts on this subject based on the ideXlab platform.
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analytical Design planning technique adept a dependency structure matrix tool to schedule the Building Design process
Construction Management and Economics, 2000Co-Authors: Simon A Austin, Andrew N Baldwin, Baizhan Li, Paul R WaskettAbstract:Current planning practice takes little account of the interdisciplinary, iterative nature of the Building Design process. This leads to a compromised Design process containing inevitable cycles of rework together with associated time and cost penalties in both Design and construction. The analytical Design planning technique (ADePT) is a planning methodology which helps to overcome these difficulties. The central part of ADePT is a dependency structure matrix (DSM). This paper describes DSM techniques and a tool developed to optimize the Design process.
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analytical Design planning technique a model of the detailed Building Design process
Design Studies, 1999Co-Authors: Simon A Austin, Andrew N Baldwin, Baizhan Li, Paul R WaskettAbstract:Abstract Current planning practice takes little account of the interdisciplinary, iterative nature of the Building Design process. This leads to a compromised Design process containing inevitable cycles of rework together with associated time and cost penalties in both Design and construction. The Analytical Design Planning Technique (ADePT) is a planning methodology which helps to overcome these difficulties. This paper describes the development of a modelling notation and model of the detailed Building Design process, which forms the first stage of ADePT.
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modelling information flow during the conceptual and schematic stages of Building Design
Construction Management and Economics, 1999Co-Authors: Andrew N Baldwin, Simon A Austin, T Hassan, A ThorpeAbstract:This paper reports on recent research the aim of which was to study, model and simulate the information flow at the conceptual and schematic stages of Building Design. The development of a generic model of the conceptual and schematic Design process for Buildings is described. This model comprising Design tasks and their information requirements was produced using data flow diagrams. Examples from several levels of the model are provided. Details are then given as to how the model may be used to assist the management of the Design process both directly and by providing primary data for other tools and techniques. Industry feedback on the data modelling and these tools and techniques is then discussed. It is concluded that it is only by a better understanding of the flow of information among project participants that the management of Design may be improved, and that although the generic data flow model provides immediate benefits to Design managers these can be enhanced greatly by the use of the model as ...
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analytical Design planning technique for programming Building Design
Proceedings of the Institution of Civil Engineers - Structures and Buildings, 1999Co-Authors: Simon A Austin, Andrew N Baldwin, Baizhan Li, Paul R WaskettAbstract:The construction process is traditionally planned either directly with bar charts, or with network analysis techniques forming the basis of the bar charts. The success of these approaches in construction planning over the years has led to their extensive use in the planning of Design. Network analysis techniques and bar charts were developed specifically to plan production processes, such as construction, that have an easily definable logic and are sequential in nature. Design, however, is an iterative processes requiring assumptions and estimates of information to be made and work to be redone until a satisfactory solution is developed. Network analysis is not therefore an appropriate basis for planning Design. They cannot account for this iterative nature, they monitor progress based upon the completion of drawing work and other Design deliverables and are inappropriate for monitoring the availability of key pieces of information.The Analytical Design Planning Technique (ADePT), shown schematically in figure 1, offers an approach to planning Design that accounts for the necessity to undertake work in an iterative manner, enables work to be monitored on the basis of the production of information, and allows Design to be fully integrated with the overall construction process1. The first stage of the ADePT methodology is a model of the detailed stage of the Building Design process, representing Design activities and their information requirements. The data in this model is linked via a dependency table to a Dependency Structure Matrix (DSM) analysis tool2 which is used in the second stage to identify iteration within the Design process and arrange the activities with the objective of optimising the task order. The third stage of the methodology produces Design programmes based on the optimised process sequence. The technique requires some iteration between the DSM and programming stages. The authors have developed computer tools to enable each stage to be undertaken in an efficient manner and thus, facilitate more effective planning and management of Building Design3. This paper reviews current problems in Design planning within the construction industry and the use of a Dependency Structure Matrix tool to order the detailed Design process. It then describes in detail the representation of the optimal Design sequence within a programme and the integration of the optimised Design programme with procurement and construction programmes.
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a data flow model to plan and manage the Building Design process
Journal of Engineering Design, 1996Co-Authors: Simon A Austin, Andrew N Baldwin, Andrew P NewtonAbstract:SUMMARY The increasing complexity of modem Buildings and the client's desire for swift occupation has meant the expeditious completion of the Design phase of a project has become ever more important. An effective and workable Design programme is essential to achieve this. Traditionally, Building Design work has been planned in a perfunctory manner, often in the belief that this creative and iterative process cannot be analyzed and planned in detail. This situation has been perpetuated by a lack of understanding of Design information flow and dependency, and the availability of suitable planning techniques. This paper describes a data flow model of the Building Design process that is subsequently analyzed in a Design structure matrix. The synthesis of these two techniques produces a powerful but easily understood tool to assist in the planning and management of complex, multi-disciplinary Building Design problems.
Paul R Waskett - One of the best experts on this subject based on the ideXlab platform.
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analytical Design planning technique adept a dependency structure matrix tool to schedule the Building Design process
Construction Management and Economics, 2000Co-Authors: Simon A Austin, Andrew N Baldwin, Baizhan Li, Paul R WaskettAbstract:Current planning practice takes little account of the interdisciplinary, iterative nature of the Building Design process. This leads to a compromised Design process containing inevitable cycles of rework together with associated time and cost penalties in both Design and construction. The analytical Design planning technique (ADePT) is a planning methodology which helps to overcome these difficulties. The central part of ADePT is a dependency structure matrix (DSM). This paper describes DSM techniques and a tool developed to optimize the Design process.
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analytical Design planning technique a model of the detailed Building Design process
Design Studies, 1999Co-Authors: Simon A Austin, Andrew N Baldwin, Baizhan Li, Paul R WaskettAbstract:Abstract Current planning practice takes little account of the interdisciplinary, iterative nature of the Building Design process. This leads to a compromised Design process containing inevitable cycles of rework together with associated time and cost penalties in both Design and construction. The Analytical Design Planning Technique (ADePT) is a planning methodology which helps to overcome these difficulties. This paper describes the development of a modelling notation and model of the detailed Building Design process, which forms the first stage of ADePT.
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analytical Design planning technique for programming Building Design
Proceedings of the Institution of Civil Engineers - Structures and Buildings, 1999Co-Authors: Simon A Austin, Andrew N Baldwin, Baizhan Li, Paul R WaskettAbstract:The construction process is traditionally planned either directly with bar charts, or with network analysis techniques forming the basis of the bar charts. The success of these approaches in construction planning over the years has led to their extensive use in the planning of Design. Network analysis techniques and bar charts were developed specifically to plan production processes, such as construction, that have an easily definable logic and are sequential in nature. Design, however, is an iterative processes requiring assumptions and estimates of information to be made and work to be redone until a satisfactory solution is developed. Network analysis is not therefore an appropriate basis for planning Design. They cannot account for this iterative nature, they monitor progress based upon the completion of drawing work and other Design deliverables and are inappropriate for monitoring the availability of key pieces of information.The Analytical Design Planning Technique (ADePT), shown schematically in figure 1, offers an approach to planning Design that accounts for the necessity to undertake work in an iterative manner, enables work to be monitored on the basis of the production of information, and allows Design to be fully integrated with the overall construction process1. The first stage of the ADePT methodology is a model of the detailed stage of the Building Design process, representing Design activities and their information requirements. The data in this model is linked via a dependency table to a Dependency Structure Matrix (DSM) analysis tool2 which is used in the second stage to identify iteration within the Design process and arrange the activities with the objective of optimising the task order. The third stage of the methodology produces Design programmes based on the optimised process sequence. The technique requires some iteration between the DSM and programming stages. The authors have developed computer tools to enable each stage to be undertaken in an efficient manner and thus, facilitate more effective planning and management of Building Design3. This paper reviews current problems in Design planning within the construction industry and the use of a Dependency Structure Matrix tool to order the detailed Design process. It then describes in detail the representation of the optimal Design sequence within a programme and the integration of the optimised Design programme with procurement and construction programmes.
Baizhan Li - One of the best experts on this subject based on the ideXlab platform.
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application of multi objective genetic algorithm to optimize energy efficiency and thermal comfort in Building Design
Energy and Buildings, 2015Co-Authors: Wei Yu, Baizhan Li, Ming Zhang, Di WangAbstract:Abstract Several conflicting criteria exist in Building Design optimization, especially energy consumption and indoor environment thermal performance. This paper presents a novel multi-objective optimization model that can assist Designers in green Building Design. The Pareto solution was used to obtain a set of optimal solutions for Building Design optimization, and uses an improved multi-objective genetic algorithm (NSGA-II) as a theoretical basis for Building Design multi-objective optimization model. Based on the simulation data on energy consumption and indoor thermal comfort, the study also used a simulation-based improved back-propagation (BP) network which is optimized by a genetic algorithm (GA) to characterize Building behavior, and then establishes a GA–BP network model for rapidly predicting the energy consumption and indoor thermal comfort status of residential Buildings; Third, the Building Design multi-objective optimization model was established by using the GA–BP network as a fitness function of the multi-objective Genetic Algorithm (NSGA-II); Finally, a case study is presented with the aid of the multi-objective approach in which dozens of potential Designs are revealed for a typical Building Design in China, with a wide range of trade-offs between thermal comfort and energy consumption.
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analytical Design planning technique adept a dependency structure matrix tool to schedule the Building Design process
Construction Management and Economics, 2000Co-Authors: Simon A Austin, Andrew N Baldwin, Baizhan Li, Paul R WaskettAbstract:Current planning practice takes little account of the interdisciplinary, iterative nature of the Building Design process. This leads to a compromised Design process containing inevitable cycles of rework together with associated time and cost penalties in both Design and construction. The analytical Design planning technique (ADePT) is a planning methodology which helps to overcome these difficulties. The central part of ADePT is a dependency structure matrix (DSM). This paper describes DSM techniques and a tool developed to optimize the Design process.
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analytical Design planning technique a model of the detailed Building Design process
Design Studies, 1999Co-Authors: Simon A Austin, Andrew N Baldwin, Baizhan Li, Paul R WaskettAbstract:Abstract Current planning practice takes little account of the interdisciplinary, iterative nature of the Building Design process. This leads to a compromised Design process containing inevitable cycles of rework together with associated time and cost penalties in both Design and construction. The Analytical Design Planning Technique (ADePT) is a planning methodology which helps to overcome these difficulties. This paper describes the development of a modelling notation and model of the detailed Building Design process, which forms the first stage of ADePT.
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analytical Design planning technique for programming Building Design
Proceedings of the Institution of Civil Engineers - Structures and Buildings, 1999Co-Authors: Simon A Austin, Andrew N Baldwin, Baizhan Li, Paul R WaskettAbstract:The construction process is traditionally planned either directly with bar charts, or with network analysis techniques forming the basis of the bar charts. The success of these approaches in construction planning over the years has led to their extensive use in the planning of Design. Network analysis techniques and bar charts were developed specifically to plan production processes, such as construction, that have an easily definable logic and are sequential in nature. Design, however, is an iterative processes requiring assumptions and estimates of information to be made and work to be redone until a satisfactory solution is developed. Network analysis is not therefore an appropriate basis for planning Design. They cannot account for this iterative nature, they monitor progress based upon the completion of drawing work and other Design deliverables and are inappropriate for monitoring the availability of key pieces of information.The Analytical Design Planning Technique (ADePT), shown schematically in figure 1, offers an approach to planning Design that accounts for the necessity to undertake work in an iterative manner, enables work to be monitored on the basis of the production of information, and allows Design to be fully integrated with the overall construction process1. The first stage of the ADePT methodology is a model of the detailed stage of the Building Design process, representing Design activities and their information requirements. The data in this model is linked via a dependency table to a Dependency Structure Matrix (DSM) analysis tool2 which is used in the second stage to identify iteration within the Design process and arrange the activities with the objective of optimising the task order. The third stage of the methodology produces Design programmes based on the optimised process sequence. The technique requires some iteration between the DSM and programming stages. The authors have developed computer tools to enable each stage to be undertaken in an efficient manner and thus, facilitate more effective planning and management of Building Design3. This paper reviews current problems in Design planning within the construction industry and the use of a Dependency Structure Matrix tool to order the detailed Design process. It then describes in detail the representation of the optimal Design sequence within a programme and the integration of the optimised Design programme with procurement and construction programmes.
Andrew N Baldwin - One of the best experts on this subject based on the ideXlab platform.
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analytical Design planning technique adept a dependency structure matrix tool to schedule the Building Design process
Construction Management and Economics, 2000Co-Authors: Simon A Austin, Andrew N Baldwin, Baizhan Li, Paul R WaskettAbstract:Current planning practice takes little account of the interdisciplinary, iterative nature of the Building Design process. This leads to a compromised Design process containing inevitable cycles of rework together with associated time and cost penalties in both Design and construction. The analytical Design planning technique (ADePT) is a planning methodology which helps to overcome these difficulties. The central part of ADePT is a dependency structure matrix (DSM). This paper describes DSM techniques and a tool developed to optimize the Design process.
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analytical Design planning technique a model of the detailed Building Design process
Design Studies, 1999Co-Authors: Simon A Austin, Andrew N Baldwin, Baizhan Li, Paul R WaskettAbstract:Abstract Current planning practice takes little account of the interdisciplinary, iterative nature of the Building Design process. This leads to a compromised Design process containing inevitable cycles of rework together with associated time and cost penalties in both Design and construction. The Analytical Design Planning Technique (ADePT) is a planning methodology which helps to overcome these difficulties. This paper describes the development of a modelling notation and model of the detailed Building Design process, which forms the first stage of ADePT.
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modelling information flow during the conceptual and schematic stages of Building Design
Construction Management and Economics, 1999Co-Authors: Andrew N Baldwin, Simon A Austin, T Hassan, A ThorpeAbstract:This paper reports on recent research the aim of which was to study, model and simulate the information flow at the conceptual and schematic stages of Building Design. The development of a generic model of the conceptual and schematic Design process for Buildings is described. This model comprising Design tasks and their information requirements was produced using data flow diagrams. Examples from several levels of the model are provided. Details are then given as to how the model may be used to assist the management of the Design process both directly and by providing primary data for other tools and techniques. Industry feedback on the data modelling and these tools and techniques is then discussed. It is concluded that it is only by a better understanding of the flow of information among project participants that the management of Design may be improved, and that although the generic data flow model provides immediate benefits to Design managers these can be enhanced greatly by the use of the model as ...
-
analytical Design planning technique for programming Building Design
Proceedings of the Institution of Civil Engineers - Structures and Buildings, 1999Co-Authors: Simon A Austin, Andrew N Baldwin, Baizhan Li, Paul R WaskettAbstract:The construction process is traditionally planned either directly with bar charts, or with network analysis techniques forming the basis of the bar charts. The success of these approaches in construction planning over the years has led to their extensive use in the planning of Design. Network analysis techniques and bar charts were developed specifically to plan production processes, such as construction, that have an easily definable logic and are sequential in nature. Design, however, is an iterative processes requiring assumptions and estimates of information to be made and work to be redone until a satisfactory solution is developed. Network analysis is not therefore an appropriate basis for planning Design. They cannot account for this iterative nature, they monitor progress based upon the completion of drawing work and other Design deliverables and are inappropriate for monitoring the availability of key pieces of information.The Analytical Design Planning Technique (ADePT), shown schematically in figure 1, offers an approach to planning Design that accounts for the necessity to undertake work in an iterative manner, enables work to be monitored on the basis of the production of information, and allows Design to be fully integrated with the overall construction process1. The first stage of the ADePT methodology is a model of the detailed stage of the Building Design process, representing Design activities and their information requirements. The data in this model is linked via a dependency table to a Dependency Structure Matrix (DSM) analysis tool2 which is used in the second stage to identify iteration within the Design process and arrange the activities with the objective of optimising the task order. The third stage of the methodology produces Design programmes based on the optimised process sequence. The technique requires some iteration between the DSM and programming stages. The authors have developed computer tools to enable each stage to be undertaken in an efficient manner and thus, facilitate more effective planning and management of Building Design3. This paper reviews current problems in Design planning within the construction industry and the use of a Dependency Structure Matrix tool to order the detailed Design process. It then describes in detail the representation of the optimal Design sequence within a programme and the integration of the optimised Design programme with procurement and construction programmes.
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a data flow model to plan and manage the Building Design process
Journal of Engineering Design, 1996Co-Authors: Simon A Austin, Andrew N Baldwin, Andrew P NewtonAbstract:SUMMARY The increasing complexity of modem Buildings and the client's desire for swift occupation has meant the expeditious completion of the Design phase of a project has become ever more important. An effective and workable Design programme is essential to achieve this. Traditionally, Building Design work has been planned in a perfunctory manner, often in the belief that this creative and iterative process cannot be analyzed and planned in detail. This situation has been perpetuated by a lack of understanding of Design information flow and dependency, and the availability of suitable planning techniques. This paper describes a data flow model of the Building Design process that is subsequently analyzed in a Design structure matrix. The synthesis of these two techniques produces a powerful but easily understood tool to assist in the planning and management of complex, multi-disciplinary Building Design problems.
Sundaravelpandia Singaravel - One of the best experts on this subject based on the ideXlab platform.
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component based machine learning for performance prediction in Building Design
Applied Energy, 2018Co-Authors: Philipp Geye, Sundaravelpandia SingaravelAbstract:Abstract Machine learning is increasingly being used to predict Building performance. It replaces Building performance simulation, and is used for data analytics. Major benefits include the simplification of prediction models and a dramatic reduction in computation times. However, the monolithic whole-Building models suffer from a limited transfer of models and their data to other contexts. This imposes a vital limitation on the application of machine learning in Building Design. In this paper, we present a component-based approach that develops machine learning models not only for a parameterized whole Building Design, but for parameterized components of the Design as well. Two decomposition levels, namely construction level components (wall, windows, floors, roof, etc.), and zone-level components, are examined. Results in test cases show that, depending on how far the cases deviate from the training case and its data, high prediction quality may be achieved with errors as low as 3.7% for cooling and 3.9% for heating.
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component based machine learning for energy performance prediction by multilod models in the early phases of Building Design
Workshop of the European Group for Intelligent Computing in Engineering, 2018Co-Authors: Philipp Geye, Manav Maha Singh, Sundaravelpandia SingaravelAbstract:The application of Building information modeling (BIM) in early Design phases requires the support of different levels of detail (LOD). This allows scaling to be supported as an important activity of Designing. Furthermore, to achieve well-performing solutions in terms of energy efficiency, it is necessary to consider energy performance in early Design stages. Therefore, this paper presents a multiLOD modeling approach for the early phases of Building Design that integrates energy performance prediction based on component-based machine learning (ML) using artificial neural networks (ANN). A model structure with three adaptive LOD definitions is proposed to support the Design process by a digital model that supports flexible scaling back and forth. By linking the ML models to the elements in this structure, components are formed that support quick and flexible modeling and energy performance prediction in the early Building Design process. The transformation rules flexibly link the ML components to all LOD. This approach was illustrated and validated by a test case with a medium-sized office Building. The early Design states of the case were reconstructed for the application of the method. For validation purposes, the results of the ML predictions for 60 different Design configurations were compared to those of a conventional parametric full-detail simulation model. This comparison showed that the average error was no higher than 3.8% for heating and 3.5% for cooling.
