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Works dealing with Scan-to-BIM have, to date, principally focused on 'structural' components such as floors, ceilings and walls (with doors and windows). But the control of new facilities and the production of their corresponding as-is BIM models requires the identification and inspection of numerous other building components and objects, e.g. MEP components, such as plugs, switches, ducts, and signs. In this paper, we present a new 6D-based (XYZ + RGB) approach that processes dense coloured 3D points provided by terrestrial laser scanners in order to recognize the aforementioned smaller objects that are commonly located on walls. This paper focuses on the recognition of objects such as sockets, switches, signs, extinguishers and others. After segmenting the point clouds corresponding to the walls of a building, a set of candidate objects are detected independently in the colour and geometric spaces, and an original consensus procedure integrates both results in order to infer recognition. Finally, the recognized object is positioned and inserted in the as-is semantically-rich 3D model, or BIM model. The assessment of the method has been carried out in simulated scenarios under virtual scanning providing high recognition rates and precise positioning results. Experimental tests in real indoors using our MoPAD (Mobile Platform for Autonomous Digitization) platform have also yielded promising results. [ABSTRACT FROM AUTHOR]

Abstract: Radio frequency identification (RFID) is an emerging technology in the building industry. Many researchers have demonstrated how to enhance material control or production management with RFID. However, there is a lack of integrated understanding of lifecycle management. This paper develops and demonstrates a framework to Information Lifecycle Management (ILM) with RFID for material control. The ILM framework includes key RFID checkpoints and material types to facilitate material control on construction sites. In addition, this paper presents a context-aware scenario to examine multiple on-site context and RFID parameters. From tagging nodes at the factory to reading nodes at each lifecycle stage, this paper demonstrates how to manage complex construction materials with RFID and how to construct integrated information flows at different lifecycle stages. To validate key material types and the scenario, the study reports on two on-site trials: read distance test and on-site simulation. Finally, the research provides discussion and recommended approaches to implementing ILM. The results show that the ILM framework has the potential for a variety of stakeholders to adopt RFID in the building industry. This paper provides the understanding about the effectiveness of ILM with RFID for material control, which can serve as a base for adopting other IT technologies in the building industry. [Copyright &y& Elsevier]

Abstract: This paper presents results of the first phase of the research project “Serious Human Rescue Game” at Technische Universität Darmstadt. It presents a new serious gaming approach based on Building Information Modeling (BIM) for the exploration of the effect of building condition on human behavior during the evacuation process. In reality it is impossible to conduct rescue tests in burning buildings to study the human behavior. Therefore, the current methods of data-collecting for existing evacuation simulation models have limitations regarding the individual human factors. To overcome these limitations the research hypothesis is that the human behavior can be explored with a serious computer game: The decisions of a person during the game should be comparable to decisions during an extreme situation in the real world. To verify this hypothesis, this paper introduces a serious gaming approach for analyzing the human behavior in extreme situations. To implement a serious game, developers generally make use of 3D-modeling software to generate the game content. After this, the game logic needs to be added to the content with special software development kits for computer games. Every new game scenario has to be built manually from scratch. This is time-consuming and a great share of modeling work needs to be executed twice (e.g., 3D-modeling), at first by the architect for the parametric building model and the second time by the game designer for the 3D-game content. The key idea of the presented approach is to use the capabilities of BIM together with engineering simulations (fire, smoke) to build realistic serious game scenarios in a new and efficient way. This paper presents the first phase results of the research project mainly focusing on the conceptual design of the serious game prototype. The validation concept is also presented. The inter-operability between building information modeling applications and serious gaming platforms should allow different stakeholders to simulate building-related scenarios in a new, interactive and efficient way. [Copyright &y& Elsevier]

Abstract: Interoperability of software is a critical requirement in the architecture, engineering, and construction (AEC) industry, where a number of data exchange standards have been created to enable data exchange among different software packages. To be able to comply with existing data exchange standards, the software developers need to match their internal data schemas to the schema defined in a standard and vice versa. The process of matching two large scale data models is time consuming and cumbersome when performed manually, and becomes even more challenging when a source and/or a target model is being updated frequently to meet the ever expanding real world requirements. While several prior studies discussed the need for approaches toward automated or semi-automated schema matching, an approach that builds on existing matches between two models has rarely been studied. In this paper, we present a semi-automated approach for model matching. This approach leverages a given set of existing matching between two models and upgrades those matching when a new version of a target model is released. The paper describes in detail a list of upgrade patterns generated and validated through a prototype by matching a domain-specific data model to several recent releases of the industry foundation classes. [Copyright &y& Elsevier]

Abstract: The field of building energy management, which monitors and analyses the energy use of buildings with the aim to control and reduce energy expenditure, is seeing a rapid evolution. Automated meter reading approaches, harvesting data at hourly or even half-hourly intervals, create a large pool of data which needs analysis. Computer analysis by means of machine learning techniques allows automated processing of this data, invoking expert analysis where anomalies are detected. However, machine learning always requires a historical dataset to train models and develop a benchmark to define what constitutes an anomaly. Computer analysis by means of building performance simulation employs physical principles to predict energy behaviour, and allows the assessment of the behaviour of buildings from a pure modelling background. This paper explores how building simulation approaches can be fused into energy management practice, especially with a view to the production of artificial bespoke benchmarks where historical profiles are not available. A real accommodation block, which is subject to monitoring, is used to gather an estimation of the accuracy of this approach. The findings show that machine learning from simulation models has a high internal accuracy; comparison with actual metering data shows prediction errors in the system (20%) but still achieves a substantial improvement over industry benchmark values. [Copyright &y& Elsevier]

Abstract: The high advanced techniques in parallel computing can be employed for a better understanding of groundwater flow fluids. Generally, the geological media are very heterogeneous and contain complex structures. Decomposing these structures into, approximately, equivalent sub-structures for a load-balancing is a major challenge. This paper proposes and analyses a new algorithm to simulate parallel flow fluid in such complex media. Fully parallel software is developed, and two well-known sparse linear solvers, based respectively on a multifrontal Cholesky factorization and an iterative structured multigrid method, are compared. The mixed finite element (MFE) method is used to discretize Darcy’s equation. Numerical examples are presented to show the efficiency and robustness of the algorithm proposed. [Copyright &y& Elsevier]

Abstract: Structural design and optimization in engineering are increasingly addressing non-standard optimization problems (NSPs). These problems are characterized by a complex topology of the optimization space with respect to nonlinearity, multimodality, discontinuity, etc. By that, NSP can only be solved by means of computer simulations. In addition, the corresponding numerical approaches applied often tend to be noisy. Typical examples for NSP occur in robust optimization, where the solution has to be robust with respect to implementation errors, production tolerances or uncertain environmental conditions. However, a generally applicable strategy for solving such problem categories always equally efficiently is not yet available. To improve the situation, a distributed agent-based optimization approach for solving NSPs is introduced in this paper. The elaborated approach consists of a network of cooperating but also competing strategy agents that wrap various strategies, especially optimization methods (e.g. SQP, DE, ES, PSO, etc.) using different search characteristics. In particular, the strategy agents contain an expert system modeling their specific behavior in an optimization environment by means of rules and facts on a highly abstract level. Further, different common interaction patterns have been defined to describe the structure of a strategy network and its interactions. For managing the complexity of NSPs using multi-agent systems (MASs) efficiently, a simulation and experimentation platform has been developed. Serving as a computational steering tool, it applies MAS technology and accesses a network of various optimization strategies. As a consequence, an elegant interactive steering, a customized modeling and a powerful visualization of structural optimization processes are established. To demonstrate the far reaching applicability of the proposed approach, numerical examples are discussed, including nonlinear function and robust optimization problems. The results of the numerical experiments illustrate the potential of the agent-based strategy network approach for collaborative solving, where observed synergy effects lead to an effective and efficient solution finding. [Copyright &y& Elsevier]

Abstract: It is expected by enterprises that design changes that occur in a complex product development process can be resolved in a cost-effective way so that products modified for new customer requirements or new technology can be launched into the market without increasing much cost and time. Usually there are different solutions or methods for design changes, which may have different impacts on the whole process. Since a complex product development process consists of many design tasks and different relationships among these tasks, it is not easy to analytically compute the completion time when change propagation finishes. In this paper a process simulation based method is proposed to select the most economic propagation path for each design change, which can reduce the total process time for changes occurring in the complex product development process. “And/Or” graph nodes are used to represent the input and output logics of each task in the design process. Mathematic models are developed to calculate the completion time of the change propagation process. Monte Carlo based simulation algorithms for finding feasible output paths and calculating impacts of input changes on tasks are developed in order to simulate the change propagation process. The objective function for scheduling design change propagations is obtained by the regression analysis of simulation results. And the best change propagation paths, which usually are not the real optimum of the original design process model, are given by optimizing the regression model with a commercial optimization package. The effect of the method is demonstrated with a study of change propagations in the motor cycle engine design process. [Copyright &y& Elsevier]

Abstract: Supervisory control theory enables control system designers to specify a model of the uncontrolled system in combination with control requirements, and subsequently use a synthesis algorithm for automatic controller generation. The use of supervisory control synthesis can significantly reduce development time of supervisory controllers as a result of unambiguous specification of control requirements, and synthesis of controllers that by definition are nonblocking and satisfy the control requirements. This is especially important for evolving systems, where requirements change frequently. For successful industrial application, the specification formalism should be expressive and intuitive enough to be used by domain experts, who define control requirements, and software experts, who implement control requirements and synthesize controllers. This paper defines such a supervisory control specification formalism that consists of automata, synchronizing actions, guards, updates, invariants, independent and dependent variables, where the values of the dependent variables can be defined in terms of functions on the independent variables. We also show how the language enables systematic, compositional specification of a control system for a patient communication system of an MRI scanner. We show that our specification formalism can deal with both event-based and state-based interfaces. To support systematic, modular specification of models for supervisory control synthesis, we introduce state trackers that record sequences of events in terms of states. The synthesized supervisor has been successfully validated by means of interactive user guided simulation. [Copyright &y& Elsevier]

Abstract: In this paper we discuss the benefits derived by combining parametric modeling and genetic algorithms to achieve a performance oriented process in design, with specific focus on architectural design. The key role played by geometry in architecture is discussed in relation to performance oriented design, in which evaluations based on engineering criteria are integrated into the conceptual phase of the design. The performance attained by a specific geometric solution is considered along with its complexity in an interdisciplinarity process. A specific case study using large roofs is presented as an example. Enabling the designer to automatically generate a large range of alternative design solutions is a great advantage offered by parametric modeling in supporting geometric design explorations. However, this in turn presents the difficulty of how to evaluate the resulting myriad of generated alternatives. ParaGen is presented as a tool to support the exploration of the parametric design alternatives. ParaGen combines parametric modeling, performance simulation software and genetic algorithms, together with a database to store and retrieve the solutions for subsequent exploration. The design exploration is enhanced by means of the interaction of the designer with the process. This serves two objectives. Firstly, it addresses the genetic algorithm based creation of design solutions, while still focusing on a given fitness function. Secondly, it facilitates knowledge extraction from the generated solutions. A description of the tool and its possible uses by designers is provided. Applications of this tool are illustrated for both education and research, with specific reference to two examples in the field of modular long span roofs. The first case study has been developed as part of a teaching exercise in which ParaGen is used to explore the morphology of a dome based on structural performance. The second case study is derived from a research project which deals with solar energy transmission, and concerns the solar heat gain and daylight transmittance of a long span roof. [Copyright &y& Elsevier]

Abstract: In this paper, a new experimental approach – computer simulation – is introduced for understanding design activities and for validating design theories. Following the generic framework of computer simulation, three main components in simulating design activities are introduced: mathematical model, simulation model, and statistical analysis. The mathematical model consists of the design governing equation and Environment-Based Design (EBD), based on which three routes are introduced to look for new design solutions: (1) formulating the design problem differently at the beginning of a design process may get quite different solutions, in which creative design could emerge; (2) extending designer’s knowledge and experience can help generate more candidate solutions, and so increasing the probability of generating a good concept; (3) changing the sequence of design problem decomposition may change product requirements, and thus change the generated design concepts. By viewing mesh generation algorithms as design agents, a computer simulation environment is used to study design activities. Statistical analysis is conducted to validate quantitatively the three routes to new design solutions. The results show that computer simulation is an effective approach to studying design activities. [Copyright &y& Elsevier]

Abstract: In a manufacturing system, we need to capture collaborative processes among its components in order to clearly define supporting functions of a system. However, pervasive process modeling techniques, including IDEF3, Petri Nets, and UML, are not sufficient for modeling collaborative processes. Therefore, we have developed a novel modeling method referred to as collaborative process modeling (CPM) to describe collaborative processes. CPM models can be transformed into marked graph models so that we can use the analysis power of Petri Nets. In this paper, we first briefly discuss these process modeling techniques. Then, we illustrate the CPM method and transformation rules with illustrative examples. CPM allows us to develop collaborative process models, understand and facilitate the realization of collaboration, and verify models before moving onto development. [Copyright &y& Elsevier]

Abstract: Ontological analyses have been used in numerous publications to compare existing modelling grammars with an ontology. However, a sound theoretical research framework is still missing. Consequently, working with the results of such ontological analyses is theoretically questionable. Therefore, the aim of this paper is threefold. Firstly, we want to contribute to such a theoretical research framework by formalising the ontological analyses approach as sets of mathematical functions between ontological and modelling grammar constructs. Secondly, on this basis, we derive four formal requirements each ontological analyses must comply with. Thirdly, we analyse whether current state of the art ontological analyses comply with our findings. While the formalisation demonstrates the strengths of the ontological analysis approach we conclude that current analyses have theoretical deficiencies, which lead to serious limitations in their application. Lastly, we demonstrate the usefulness of our formal framework by describing a new application. This application uses two different ontological analyses as input and produces mappings between the modelling grammars used in these analyses as output. [Copyright &y& Elsevier]

Abstract: This paper introduces a design methodology of a fault-tolerant autonomous multi-robot system (MRS). An important fundamental topic for this type of system is the design of an on-line autonomous behavior acquisition mechanism that is capable of developing cooperative roles as well as assigning them to a robot appropriately in a noisy embedded environment. Our approach is to apply reinforcement learning that adopts the Bayesian discrimination method for segmenting a continuous state space and a continuous action space simultaneously. In addition, a neural network is provided for predicting the average of the other robots’ postures at the next time step in order to stabilize the reinforcement-learning environment. Computer simulations are conducted to illustrate the fault-tolerance of our MRS against a system change that occurs after the MRS achieves stable behavior. [Copyright &y& Elsevier]

Abstract: This paper presents a method for securing collaborative design using multi-level modeling. When a team of designers works collaboratively on a 3D assembly model, a component of the assembly is presented in a full detail to those who have full access privileges to the component, but in an abstract level of detail to those who have less access privileges. Such various levels of detail can be created in two phases: volumetric feature removal which is achieved through interactive feature recognition on the CAD model, and multi-resolution mesh construction which is based on polygonal simplification. Appropriate representations of the assembly are extracted by direction of access matrix, and then presented to the users participating in collaborative design. The key issues in developing the secure collaborative design system are discussed, and the implementation results are reported. [Copyright &y& Elsevier]

This paper presents a generalized agent-based framework that uses negotiation to dynamically and optimally schedule events. Events can be created dynamically by any active agent in the environment. Each event may potentially require collaboration or resources from one or more other agents. The allocation of resources to the event will be negotiated iteratively until a compromise is found. The framework consists of a user preference model, an evaluation or utility function, and a negotiation protocol. The negotiation protocol is used to implement a distributed negotiation algorithm, called Nstar (N*). N* is based conceptually on the A* algorithm for optimal search but extended for distributed negotiation. N* is a general distributed negotiation algorithm that makes use of negotiation strategies to find solutions that meet different negotiation objectives. For example, it can use a utility optimizing strategy to find the solution that maximizes average utilities of individual agents. Alternatively, it can select a time optimizing strategy to locate a ‘quick’ feasible solution. The negotiation protocol also performs conflict resolution using a form of iterative repair that renegotiates events which have conflicts. A special case of this framework was used in our MAFOA (mobile agents for office automation) environment to perform agent-based meeting scheduling. A computer simulation test bed was built to simulate the scheduling of hundreds of randomly generated meetings using our N* algorithm. [Copyright &y& Elsevier]

This paper explores the use of artificial neural networks (ANNs) as a valid alternative to the traditional job-shop simulation approach. Feed forward, multi-layered neural network metamodels were trained through the back-error-propagation (BEP) learning algorithm to provide a versatile job-shop scheduling analysis framework. The constructed neural network architectures were capable of satisfactorily estimating the manufacturing lead times (MLT) for orders simultaneously processed in a four-machine job shop. The MLTs produced by the developed ANN models turned out to be as valid as the data generated from three well-known simulation packages, i.e. Arena, SIMAN, and ProModel. The ANN outputs proved not to be substantially different from the results provided by other valid models such as SIMAN and ProModel when compared against the adopted baseline, Arena. The ANN-based simulations were able to fairly capture the underlying relationship between jobs'' machine sequences and their resulting average flowtimes, which proves that ANNs are a viable tool for stochastic simulation metamodeling. [Copyright &y& Elsevier]