Hybrid Simulation for Construction Operations

Developing realistic and unbiased simulation models for construction operations require addressing the operational and strategic decision making levels. The dynamics and feedback processes observed in construction systems are responsible for the real behavior of such systems and drive the needs for hybrid and integrated simulation tools. The dominant simulation methods such as discrete event simulation (DES) and system dynamics (SD) are limited individually of capturing all the significant construction operation aspects that are responsible for generating the behaviour of realistic models. Therefore, this thesis presents a hybrid simulation method for simulating construction operations by utilizing the joint powerful features of the DES and SD methods. The proposed method provides a framework to integrate DES and SD on single computational platform. Developing a hybrid simulation model commences by decomposing the construction project into units, form which simulation models (e.g. DES or SD) are developed. A unidirectional variables interaction from DES to SD models is used. The interfacing process among simulation models is achieved by defining three variables: sender, interface, and receiver. The mechanism that controls data mapping processes between variables is outlined in a new developed synchronization method. The variables interaction protocol is described using formalism. Finally, a Hybrid Simulation Application (HiSim) is coded in VB.NET to demonstrate a sequential implementation of the developed method. A real-world earthmoving project is modeled and simulated to test the developed hybrid simulation method. The hybrid simulation structure uses unidirectional and sequential interactions between the components of DES and SD models. The simulation is run under three scenarios, is able to predict the real project completion duration with 92% accuracy, and captures the influences of the context level variables. The findings are expected to enhance hybrid simulation applications in construction and to allow for better understanding of the impact of various internal and external factors on the project schedule and its productivity performance

Stochastic analysis of flow and salt transport modeling in irrigation-drainage systems

2012 Spring.Includes bibliographical references.Sustainability of crop production in the Lower Arkansas River Basin in Colorado is seriously threatened by the continuous degradation of irrigated lands by the dual impact of soil salinization and waterlogging problems. Integration of improved irrigation practices, upgrades to the irrigation systems, and subsurface drainage are essential components of any plan to stop the deterioration of irrigated lands. Numerical simulations of irrigation and drainage systems are necessary to justify the consequent management actions. Despite the uncertainty of their predictions, numerical models are still indispensable decision support tools to investigate the feasibility of irrigation and drainage systems management plans. However, the uncertainties in input parameters to these models create a risk of misleading numerical results. That is beside the fact that the numerical models themselves are conceptual simplifications of the complex reality. The overarching objective of this dissertation is to investigate the impact of parameters uncertainty on the response of simulated irrigation-drainage systems. In the first part of the research, a Global Sensitivity Analysis (GSA) is conducted using a one-dimensional variably saturated problem to prioritize parameters according to their importance with respect to predefined performance indices. A number of GSA methods are employed for this purpose, and their comparative performances are investigated. Results show that only five parameters out of 18 parameters are responsible for around 73% of crop yield uncertainty. The second part introduces a method to reduce the computational requirements of Monte Carlo Simulations. Numerical simulation of variably saturated three-dimensional fields is typically a computationally intensive process, let alone Monte Carlo Simulations of such problems. In order to reduce the number of model evaluations while producing acceptable estimates of the output statistical properties, Cluster Analysis (CA) is used to group the input parameter realizations, e.g. hydraulic conductivity. The potentials of this approach are investigated using different: 1) clustering schemes; 2) clustering configurations, and 3) subsampling schemes. . Results show that response of 400 realizations ensemble can be efficiently approximated using selected 50 realizations. The third part of the research investigates the impact of input parameter uncertainty on the response of irrigation-drainage systems, particularly on crop yield and root zone hydrosalinity. The three-dimensional soil parameters, i.e. hydraulic conductivity, porosity, the pore size distribution (van Genuchten β) parameter, the inverse of the air entry pressure (van Genuchten α) parameter, the residual moisture content parameter, and dispersivity; are treated as spatial random processes. A sequential multivariate Monte Carlo simulation approach is implemented to produce correlated input parameter realizations. Other uncertain parameters that are considered in the study are irrigation application variability, irrigation water salinity, irrigation uniformity, preferential flow fraction, drain conductance coefficient, and crop yield model parameters. Results show that as the crop sensitivity to salinity increases, the crop yield standard deviation increases. The fourth part of the research investigates an approach for optimal sampling of multivariate spatial parameters in order to reduce their uncertainty. The Ensemble Kalman Filter is used as instrumentation to integrate the sampling of the hydraulic conductivity and the water level for a two-dimensional steady state problem. The possibility of combining designs for efficient prediction and for efficient geostatistical parameter estimation is also investigated. Moreover, the effect of relative prices of sampled parameters is also investigated. A multi-objective genetic algorithm is employed to solve the formulated integer optimization problem. Results reveal that the multi-objective genetic algorithm constitutes a convenient framework to integrate designs that are efficient for prediction and for geostatistical parameter estimation