Computer Aided Civil and Infrastructure Engineering

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

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Multiagent systems: a modern approach to distributed artificial intelligence

The problem of managing effective pedestrian evacuation during disruptions is a significant challenge because of the complexity and randomness of the passenger flow dynamics as well as the difficul...

A hybrid simulation model of passenger emergency evacuation under disruption scenarios: A case study of a large transfer railway station

Wireless sensor networks (WSNs) generate a variety of continuous data streams. To reduce data storage and transmission cost, compression is recommended to be applied to the data streams from every single sensor node. Local compression falls into two categories: lossless and lossy. Lossy compression techniques are generally preferable for sensors in commercial nodes than the lossless ones as they provide a better compression ratio at a lower computational cost. However, the traditional approaches for data compression in WSNs are sensitive to sensor accuracy. They are less efficient when there are abnormal and faulty measurements or missing data. This paper proposes a new lossy compression approach using the Bayesian predictive coding (BPC). Instead of the original signals, predictive coding transmits the error terms which are calculated by subtracting the predicted signals from the actual signals to the receiving node. Its compression performance depends on the accuracy of the adopted prediction technique. BPC combines the Bayesian inference with the predictive coding. Prediction is made by the Bayesian inference instead of regression models as in traditional predictive coding. In this way, it can utilize prior information and provide inferences that are conditional on the data without reliance on asymptotic approximation. Experimental tests show that the BPC is the same efficient as the linear predictive coding when handling independent signals which follow a stationary probability distribution. More than that, the BPC is more robust toward occasionally erroneous or missing sensor data. The proposed approach is based on the physical knowledge of the phenomenon in applications. It can be considered as a complementary approach to the existing lossy compression family for WSNs.

A new lossy compression algorithm for wireless sensor networks using Bayesian predictive coding

Modeling and discrete event simulation in industrial systems considering consumption and electrical energy generation

Computer simulation has defined itself as a reliable method for the analysis of stochastic and dynamic complex systems in both academic and practical applications. This is largely attributed to the advent and evolution of several simulation taxonomies, such as, Discrete Event Simulation, Continuous Simulation, System Dynamics, Agent-Based Modeling, and hybrid approaches, e.g., combined discrete-continuous simulation, etc. Each of these simulation methods works best for certain types of problems. In this paper, a discrete-continuous simulation approach is described for studying train and pedestrian traffic interactions for purposes of decision support. A practical operations problem related to commodity train operation within two small towns in Alberta, Canada, is then used to demonstrate the implementation of the approach within the Simphony.NET simulation system. Simulation results generated are presented.

A combined discrete-continuous simulation model for analyzing train-pedestrian interactions

Due to cost constraints, geological conditions are investigated using boreholes. However, this means conditions are never known exactly, particularly for deep and long tunnels, because uncertainties exist between neighboring boreholes. Simulation can deal with underlying uncertainty, and offers benefits to project planners in the development of better alternatives and optimization. This research developed a simulation model using Bayes theorem and Markov chain, aiming to continuously update geological conditions of one-meter sections for tunnel construction, given the geological condition of the previous one-meter section is observed as construction progresses. An actual tunneling project is used as a case study to demonstrate the applicability of the developed methodology. The impacts are analyzed and discussed in detail. The simulation results show that continuous updates during construction can significantly improve prediction of project performance by eliminating uncertainty in the original assumption. The model can be expanded to predict results of future geologic exploration programs.

/pdf/updating-geological-conditions-using-bayes-theorem-and-10ijkjlktg.pdf

Updating geological conditions using bayes theorem and markov chain

Ground conditions remain an uncertain factor in tunneling projects, complicating the ability of practitioners to reliably estimate project productivity and, in turn, duration. This study proposes a Bayesian-based approach to incorporate real-time project data into simulation-based ground prediction models to improve prediction accuracy. Changes in ground conditions are modeled using a Hidden Markov Model, which is updated with actual project data using the Baum-Welch algorithm. The prediction model is then incorporated in Simphony.NET to enhance simulation of tunneling construction operations. A case study conducted in Edmonton, Canada, demonstrates that the proposed approach is capable of incorporating real-time data in a manner that resulted in enhanced duration prediction accuracy.

Improving tunneling simulation using bayesian updating and hidden Markov chains

Tunnel Boring Machine (TBM) tunneling projects are frequently hit with delays which can cause adverse effects, extending schedules and incurring additional costs. This paper outlines a case study to show how simulation can be effectively used to analyze productivity performance of a project with emphasis on delays from equipment breakdowns and unexpected conditions. Data collected from this project under a Method Productivity Delay Modelling study, completed by a consulting firm, was collected and prepared to model delays on a combined discrete event continuous tunneling simulation model. Calibration was done to the theoretical tunneling model to ensure the results would be reflective of the actual construction project and to measure the effectiveness of the delay modelling. Sensitivity analysis was conducted to distinguish the most unfavourable delays to a tunneling project, allowing further analysis into the results of the mitigation of these delays on project duration and hypothetical costs.

/pdf/simulation-case-study-modelling-distinct-breakdown-events-1u2lz24qrs.pdf

Simulation case study: modelling distinct breakdown events for a tunnel boring machine excavation

In this paper, we discuss a long-term planning approach for semiconductor supply chains. Since a single semiconductor wafer fabrication facility (wafer fab) needs a huge amount of energy to work as intended, the approach considers sustainability goals and production-oriented objectives. We are interested in determining how many wind turbines and solar photovoltaics need to be installed in a wafer fab to obtain a certain penetration of renewable energy while the prescribed demand is met. A mixed integer linear programming (MILP) model is established for a set of wafer fabs that work in parallel. Computational experiments are carried out to demonstrate the behavior of the model under certain experimental conditions.

A Planning Model for Incorporating Renewable Energy Sources into Semiconductor Supply Chains

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