Symeon E. Christodoulou

Toward automated generation of parametric BIMs based on hybrid video and laser scanning data

Only very few constructed facilities today have a complete record of as-built information. Despite the growing use of Building Information Modelling and the improvement in as-built records, several more years will be required before guidelines that require as-built data modelling will be implemented for the majority of constructed facilities, and this will still not address the stock of existing buildings. A technical solution for scanning buildings and compiling Building Information Models is needed. However, this is a multidisciplinary problem, requiring expertise in scanning, computer vision and videogrammetry, machine learning, and parametric object modelling. This paper outlines the technical approach proposed by a consortium of researchers that has gathered to tackle the ambitious goal of automating as-built modelling as far as possible. The top level framework of the proposed solution is presented, and each process, input and output is explained, along with the steps needed to validate them. Preliminary experiments on the earlier stages (i.e. processes) of the framework proposed are conducted and results are shown; the work toward implementation of the remainder is ongoing.

Minimum Moment Method for Resource Leveling Using Entropy Maximization

The minimum moment method for resource leveling is revisited and restated as an entropy-maximization problem. The minimum moment method assumes that the moment of the daily resource demands about the horizontal axis of a project’s resource histogram is a good measure of the resource utilization and that the optimal resource allocation exists when the total moment is at a minimum, thus when the resource histogram is of rectangular shape. The entropy-maximization method proposed in this paper makes use of the general theory of entropy and two of its principal properties (subadditivity and maximality) to revisit the minimum moment method for resource leveling. The entropy-maximization method presented allows for activity stretching and provides resource allocation solutions that show improvement over previous approaches. A case study is also presented that validates the results.

Risk-based asset management of water piping networks using neurofuzzy systems

Abstract The efficient and organized management of public utility networks is of paramount importance to a network’s viability and reliable functioning. One of the key components of a suitable network management strategy is the utilization of integrated risk analysis and asset management decision-support systems (DSS) that incorporate both the scientific aspects of risk-of-failure analysis for the network components but also the financial and socio-political parameters that are associated with the networks in study. The study reported on presents a neurofuzzy decision-support system for performing multi-factored risk-of-failure analysis and asset management related to urban water distribution networks. The study is based on two datasets (one from New York City and the other from the city of Limassol, Cyprus), analytical and numerical methods, and artificial intelligence techniques (artificial neural networks and fuzzy logic) that capture the underlying knowledge and transform the patterns of the network’s behavior into a knowledge-repository and a DSS. Among the findings reported on, is a methodology to assess the risk of failure in a network, the factors affecting the reliability of pipe segments, and a neurofuzzy approach to breakage-data analysis, stratification and maintenance prioritization. Pipe-breakage history, pipe material, pipe age, and pipe diameter are shown to be significant risk factors in urban water distribution networks.

Reliability Assessment of Urban Water Distribution Networks Under Seismic Loads

Presented herein is a methodology for the seismic assessment of the reliability of urban water distribution networks (UWDN) based on general seismic assessment standards, as per the American Lifelines Alliance (ALA) guidelines, and localized historical records of critical risk-of-failure metrics pertaining to the specific UWDN under assessment. The proposed methodology is applicable to UWDN under both normal or abnormal operating conditions (such as intermittent water supply), and the assessment of reliability incorporates data of past non-seismic damage, the vulnerabilities of the network components against seismic loading, and the topology of a UWDN. Historical data obtained using records of pipe burst incidents are processed to produce clustered ‘survival curves’, depicting the pipes’ estimated survival rate over time. The survival curves are then used to localize the generalized fragility values of the network components (primarily pipes), as assessed using the approach suggested by the ALA guidelines. The network reliability is subsequently assessed using Graph Theory (Djikstra’s shortest path algorithm), while the system reliability is calculated using Monte Carlo simulation. The methodology proposed is demonstrated on a simple small-scale network and on a real-scale district metered area (DMA). The proposed approach allows the estimation of the probability that a network fails to provide the desired level of service and allows for the prioritization of retrofit interventions and of capacity-upgrade actions pertaining to existing water pipe networks.

A Risk Analysis Framework for Evaluating Structural Degradation of Water Mains in Urban Settings, Using Neurofuzzy Systems and Statistical Modeling Techniques

This article presents analysis results of degradation modeling for water distribution systems in urban environments. The system is based on data from New York City (1982–2002) and application of statistical modeling techniques stemming from parametric and non-parametric survival analysis. Artificial Neural Network techniques are also employed for the identification of the underlying risk factors and their relevance to water main breaks (about 12 factors were reviewed) including age of pipe, diameter, material, location, previous breaks, etc.). The statistical models used were calibrated for different groups of pipes, and a combination of parametric (Weibull distributions) and non-parametric models (Kaplan-Meier survival curves, Epanechnikov kernels) were utilized to investigate the suitability of them to the estimation of pipe degradation and the forecasted time to next failure (break). The research aims the development of a framework of risk analysis models for the degradation of water distribution systems, and it is a prelude to a wider effort to combine such models with geographical information systems (GIS) in the operations and maintenance of large water distribution networks in urban environments.

Entropy-Based Sensor Placement Optimization for Waterloss Detection in Water Distribution Networks

The work presented herein addresses the problem of sensor placement optimization in urban water distribution networks by use of an entropy-based approach, for the purpose of efficient and economically viable waterloss incident detection. The proposed method is applicable to longitudinal rather than spatial sensing, thus to devices such as acoustic, pressure, or flow sensors acting on pipe segments. The method utilizes the maximality, subadditivity and equivocation properties of entropy, coupled with a statistical definition of the probability of sensing within a pipe segment, to assign an entropy metric to each pipe segment and subsequently optimize the location of sensors in the network based on maximizing the total entropy in the network. The method proposed is a greedy-search heuristic.

A bid‐unbalancing method for lowering a contractor's financial risk

A method is presented for unbalancing bids and optimizing the allocation of overall project profits to individual activities by considering the financial parameters of a project (bid mark‐up and projected cash flow), in conjunction with lowering the exposure to possible financial disorder in the project. The method utilizes the general concept of entropy and a variant of it (hereby termed ‘monetary entropy’, H M) as measures of a projects perceived level of disorder, in order to distribute the total bid mark‐up to the project activities. The entropy‐based bid‐unbalancing method seeks to minimize a possible financial disorder (the monetary entropy) resulting from limited monetary resources available to the project and from badly developed project cash flows. The intended primary users of the method are contractors during the initial bidding stage of a project.

Bid mark‐up selection using artificial neural networks and an entropy metric

Purpose – The purpose of the paper is to perform bid mark‐up optimisation through the use of artificial neural networks (ANN) and a metric of the selected bid mark‐ups derived entropy. The scope is to provide an alternative, entropy‐based method for bid mark‐up optimisation that improves on the analytical models of Friedman and Gates.Design/methodology/approach – The proposed method enables the incorporation of bid parameters through the use of ANNs pattern recognition capabilities and the integration of these parameters with a mark‐up selection process that relies on the entropy produced by possible mark‐up values. The entropy metric used is the product of the probability of winning over the bidders competitors multiplied by the natural logarithm of the inverse of this probability.Findings – The case study results show that the proposed entropy‐based bidding model compares favourably with the prevailing competitive bidding models of Friedman and Gates, resulting in higher optimisation with regards to ...

Vulnerability Assessment of Water Distribution Networks Considering Performance Data

AbstractA methodology is presented for the reliability assessment of urban water distribution networks (UWDN) on the basis of component analysis, network topology, and—most importantly—survival ana...

Traffic Modeling and College-Bus Routing Using Entropy Maximization

A method is presented by which traffic flow estimation between known origins and destinations can be evaluated based on a modified entropy model, and by which bus-routing optimization can be performed. The traffic flow analysis is performed by use of an entropy-based formulation of the vehicular movements of students within the domain under examination, while the perceived level of disorder caused by the numerous vehicle-student-trips in the domain under examination is subsequently used for the formulation of a policy and a bus-routing scheme in order to minimize the original entropy in the system. The entropy metric used in the scheduling optimization is related to the probability of student-trips by origin and destination, and an application of the method is illustrated via a case study of an urban university (with facilities in multiple locations) initiating bus service for its students.