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A Jafari, V Valentin, M Russell

Probabilistic Life Cycle Cost Model for Sustainable Housing Retrofit Decision-Making

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Series: w78:2014 (browse)
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Alarcon L F, Bastias A

Computer aided strategic planning

Abstract: Modelling concepts developed to analyse project strategic decisions have been extended and implemented in a computer system leading to a generalised methodology that allows modelling and evaluation of strategic decisions in almost any decision area. Some recent application areas of this modelling system are: strategic planning, evaluation of environmental policy impacts and evaluation of risks in owner contractor relationships . The system uses concepts of cross-impact analysis and probabilistic inference as the core of the analysis procedure. A modular model structure and a simplified knowledge acquisition procedure has been designed to avoid the excessive cognitive demands imposed to the users by the original cross-impact methodology. A simple questioning process is used to guide the discussion and elicit information in an ordered manner. The result is a powerful but easy to use computer modelling system where managers, or other potential users, are not exposed to the complexities of the mathematical model. The computer system is implemented in a Windows 95 platform and it provides a graphical interface to help the users in building a conceptual model for the decision problem. The model is a simplified structure of the variables and interactions that influence the decisions being analysed. Influences and interactions assessed by experts or decisions makers are stored in a knowledge base. The system provides powerful analysis capabilities, such as: sensitivity analysis, to identify the most important variables in the decision problem; scenario analysis, to test decision under different environmental conditions; prediction of selected performance outcomes; risk analysis, to identify the risk involved in different alternatives; comparative analysis of the effects of alternative actions on individual or combined performance measures; explanatory capabilities through the model causal structure; etc. The computer model can translate expertise collected from multiple experts into a prediction of significant outcomes for decision-making. The model allows management to test different combinations of options and predict expected performance impacts associated with the decisions under analysis. The use of this decision-support tool can provide valuable insights on alternative options for strategic decision-making

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Full text: content.pdf (360,296 bytes) (available to registered users only)

Series: w78:1998 (browse)
Cluster: papers of the same cluster (result of machine made clusters)
Class: class.strategies (0.068425) class.impact (0.056619) class.environment (0.054697)
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Permission to reproduce these papers has been graciously provided by Royal Institute of Technology, Stockholm, Sweden. The assistance of the editors, Prof. Bo-Christer Björk and Dr. Adina Jägbeck, is gratefully appreciated.


Anders Vennstrom, Thomas Olofsson, William Fawcett, Attila Dikbas, Esin Ergen

Determination and Costing of Sutainiable Construction Projects: Option Based Decision Support

Abstract: The building stock in Europe accounts for over 40% of the final energy consumption in the European Union. Moreover, the construction sector is one of the largest producers of industrial waste contributing 40-50% of landfill in some EU countries. A common way of creating a forward planning for optimal resource efficiency in construction project is to apply Life cycle cost (LCC) and Life Cycle Assessment (LCA) in the decision process. There are, however, difficulties in assessment of the impact of the whole project on the environment and estimating its sustainability. The EU funded 7th framework project CILECCTA sets out to develop a LCCA (Life Cycle Cost and Analysis) tool supporting the determination and costing of sustainable project strategies.Current LCC software can assist the decision making process in simulating different alternatives for the design, build, maintenance and demolition of assets – allowing both client and builder to determine the favoured alternative for them. Through linking LCC and LCA methodologies, the CILECCTA project will go one stage further by enabling an assessment of the impact of the whole project on the environment and estimating its sustainability. It will also include the recently developed new generation of Whole Life Costing (WLC) methodology including a probabilistic approach to the development of sustainable WLC strategies, using a real options approach. This paper sets out the framework of current LCCA tools and the challenges in developing a modular LCCA engine integrating asset-related data in price banks and life cycle inventories across Europe. The research leading to these results has received funding from the European Community's Program FP7/2007-2013 under grant agreement no 229061.

Keywords: Decision support, Life Cycle Cost, Life Cycle Assessment, real option analysis

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Series: w78:2010 (browse)
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C Klinzmann & D Hosser

Probabilistic Building Inspection and Life Assessment - a computer program for reliability based system assessment

Abstract: The collaborative research centre (CRC) 477 explores innovative methods for structural health monitoring. In project field A1, methods and strategies, the modular knowledge-based computer program PROBILAS (Probabilistic Building Inspection and Life ASsessment) is developed. Its main focus lies on the optimization of structural health monitoring measures. One opportunity to optimize the monitoring process is to concentrate the monitoring measures on a few critical weak points of a structure. These critical weak points are identified by using methods of the system and reliability theory. Additionally these methods provide the opportunity to evaluate and to assess the probability of failure of a system. This paper concentrates on the implementation of the described methods into PROBILAS. Especially the database model, its integration into the program modules and the calculation procedure used for reliability analysis are discussed further.

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Full text: content.pdf (329,245 bytes) (available to registered users only)

Series: w78:2005 (browse)
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Permission to reproduce these papers has been graciously provided by the Technische Universität Dresden.


Chao L C

Simulation of construction operation with direct inputs of physical factors

Abstract: The deterministic approach to estimating the production rate of a construction operation assumes constant midpoint physical attributes without addressing the effect of randomness of job conditions. On the other hand, most simulation models bypass physical factors and rely on secondorder inputs of probability distributions of task times, the judgements of which have been cited as difficult for users to make. This paper presents an alternative approach to production estimation, based on simulating directly the effects of changing job factors on task times, while addressing the probabilistic nature of construction. The neural network model is used as the computing mechanism for determining the cycle times of the equipment in given conditions and provides the basis for estimation. The obtained times are then fed directly into a discrete-event simulation model to simulate the process and establish the production capacity of the system as constrained by first-order factors. The approach is illustrated using a hypothetical excavating and hauling operation while the object-oriented programming technique is used to implement the computing procedure.

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Full text: content.pdf (72,259 bytes) (available to registered users only)

Series: w78:1999 (browse)
Cluster: papers of the same cluster (result of machine made clusters)
Class: class.analysis (0.027295) class.software development (0.021146) class.software-machine (0.005241)
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Permission to reproduce these papers has been graciously provided by the Research Press of the National Research Council of Canada. The support of the editors, particularly Dr. Dana Vanier, is gratefully appreciated.


J Du, R Liu, Y Hatipkarasulu

Cloud-Based Interactive Probabilistic Simulation for AEC Industry

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Series: w78:2014 (browse)
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Raphael B, Smith I

A probabilistic search algorithm for finding optimally directed solutions

Abstract: "Evolutionary search techniques such as Genetic Algorithms (GA) have recently gained considerable attention. They have been used for solving a wide range of problems including function optimisation and learning. In this paper, a new global search technique, called Probabilistic Global Search (PGS), is presented. Results of benchmark tests indicate that this technique performs better than genetic algorithms on a wide range of problems. PGS is a stochastic search technique. It works by generating points in the search space according to a probability distribution function (PDF) defined over the search space. Each axis is divided into a fixed number of intervals with equal probability density. The probability densities of intervals are modified dynamically so that points are generated with higher probability in regions containing good solutions. The algorithm includes four nested cycles: 1. Sampling 2. Probability updating 3. Focusing 4. Subdomain cycle In the sampling cycle (innermost cycle) a certain number of points are generated randomly according to the current PDF. Each point is evaluated by the user defined objective function and the best point is selected. In the next cycle, probabilities of regions containing good solutions are increased and probabilities decreased in regions containing less attractive solutions. In the third cycle, search is focused on the interval containing the best solution after a number of probability updating cycles, by further subdivision of the interval. In the subdomain cycle, the search space is progressively narrowed by selecting a subdomain of smaller size centred on the best point after each focusing cycle. Each cycle serves a different purpose in the search for a global optimum. The sampling cycle permits a more uniform and exhaustive search over the entire search space than other cycles. Probability updating and focusing cycles refine search in the neighbourhood of good solutions. Convergence is achieved by means of the subdomain cycle. The algorithm was tested on highly non-linear, non-separable functions in ten to hundred variables. Results are compared with those from three versions of GAs. In most cases PGS gives better results in terms of the number of times global optima were found and the number of evaluations required to find them. The application of the technique to non-parametric optimisation problems is further illustrated using an example from conceptual structural design."

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Full text: content.pdf (373,661 bytes) (available to registered users only)

Series: w78:2000 (browse)
Cluster: papers of the same cluster (result of machine made clusters)
Class: class.retrieve (0.019177) class.impact (0.015651) class.deployment (0.013039)
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Permission to reproduce these documents have been graciously provided by Icelandic Building Research Institute. The assistance of the editor, Mr. Gudni Gudnason, is gratefully appreciated


RJ Scherer & S-E Schapke

Constructing Building Information Networks from Proprietary Documents and Product Model Data

Abstract: The paper presents a novel Building Information Mining Framework (BIMF) that allows utilising building information captured in product model data as a valuable source of background knowledge in information retrieval and mining. Central to the framework is a four layered Bayesian Network adapted from probabilistic Information Retrieval models developed in the 90s. Capturing, combining and visualising the results of various text and model analyses as well as representing aspects of the current mining context, the network allows for explicitly representing content of the repository in personalisable information networks. These networks enable not only the retrieval of information from the text documents but also the explicit interlinking of the document and the product model domain to also support the understanding of the available interrelations and the exploration of new mining and integration strategies. The paper introduces the principal approach, explains the components of the basic network and suggests several further extensions that are currently still under development.

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Full text: content.pdf (458,088 bytes) (available to registered users only)

Series: w78:2005 (browse)
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Permission to reproduce these papers has been graciously provided by the Technische Universität Dresden.


Robert-Nicoud Y, Raphael B, Smith I

Decision support through multiple models and probabilistic search

Abstract: "A large number of candidate behaviour models may exist for existing civil engineering structures such as bridges. Finding the right model for explaining a given set of observations is a difficult task. Traditionally, modelling assumptions are made without adequate justifications and verifications. Manually constructing multiple models and comparing their behaviour with measurements is arduous and hence, we are developing support tools for engineers. Techniques of model composition and model reuse are used for systematically constructing and evaluating multiple models. In this paper, experiments in model construction for the Lutrive bridge in Switzerland and their results are reported. The Lutrive bridge was constructed in 1972 using the cantilever method with central hinges. It is found that the bridge continues to creep significantly even after twenty-seven years of construction. However, load tests indicate that the bridge possesses unusually high rigidity and earlier theoretical models (constructed manually) gave results that were different from displacement measurements by as much as 100%. It was necessary to evaluate different modelling possibilities in order to obtain reasonable correlation with measurement data. The approach we have used is summarised in the following steps: ? Cases consisting of candidate models are constructed manually. ? Spaces of behaviour represented by each case are defined. ? The total solution space which is a union of the spaces represented by individual cases is searched using a new probability-based algorithm. The following conclusions were drawn from the above approach: ? If a model contains enough number of parameters as observation points it may be possible to get an exact match by tuning values of parameters. However, certain models are capable of representing only a few modes of behaviour and no combination of parameter values might exist that explain the observed behaviour. ? Model creation in a domain such as structural engineering requires considerable amount of skill. Storing cases consisting of complete models is a means of reusing such expertise. ? Model construction is a search problem. Evolutionary and stochastic search techniques produce good results when used in combination with cases."

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Full text: content.pdf (466,067 bytes) (available to registered users only)

Series: w78:2000 (browse)
Cluster: papers of the same cluster (result of machine made clusters)
Class: class.impact (0.017941) class.retrieve (0.013831) class.environment (0.011624)
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Permission to reproduce these documents have been graciously provided by Icelandic Building Research Institute. The assistance of the editor, Mr. Gudni Gudnason, is gratefully appreciated


Saurabh Taneja, Burcu Akinci, James H. Garret, Lucio Soibelman, E. William East

Evaluation of Localization Algorithms for WLAN-Based Tracking to Support Facility Management Field Activities

Abstract: Facility management activities often require the capability to track and guide field personnel during routine and corrective maintenance tasks in dense indoor environments and large facilities. For example, during a water leak, a facility maintenance employee might require guidance to the nearest valve in a mechanical room. Such guidance requires accurate localization and tracking of mobile maintenance personnel in the field. The objective of this research study is to evaluate various localization algorithms for WLAN-based tracking of maintenance personnel in terms of accuracy and precision. Accuracy has been defined as the ability of a localization approach to track a person within a certain distance and precision has been defined as the ability to reproduce the required accuracy over time. The research described in this paper builds on the previous work of the authors on static user localization and utilizes the same test bed for evaluating the performance of different algorithms that utilizes WLAN technology to support mobile personnel tracking. The main motivation behind using the same test bed, which is an actively utilized building in Pittsburgh, PA, is to have the same baseline to evaluate the performance of static user localization and mobile user tracking. WLAN technology has been selected as it achieved good results for stationary personnel localization in the previous research work (Taneja et al. 2010). The authors have evaluated deterministic and probabilistic algorithms based on the fingerprinting approach (Bahl and Padmanabhan 2000) for mobile personnel tracking. The reason behind selecting fingerprinting approach is that this approach does not require line-of-sight between localization technology transmitters and mobile receivers, which was identified as a requirement in the previous research work (Taneja et al. 2010). The fingerprinting approach is further augmented by adding several filtering methods (Fox et al. 2003) to evaluate the impact of incorporating the motion characteristics of humans and the layout of the indoor environment on the accuracy and precision of the implemented algorithms. Initial assessment of the results indicate that deterministic algorithms perform better than probabilistic approaches when fingerprint data is limited, and incorporating the motion characteristics of humans and the layout of the indoor environment by implementing filtering methods, increases the accuracy and precision of mobile personnel tracking.

Keywords: Facility Management, Mobile Tracking, Indoor Positioning, WLAN, Fingerprinting

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Series: w78:2010 (browse)
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