Framework for Building Intelligent Simulation Models of Construction Operations

Modeling and analyzing construction operations using simulation techniques allows researchers to capture the uncertainty and randomness usually associated with these operations and can thus be an effective tool for analysis and improvement. However, the effort and knowledge required to build simulation models and experiment with them tend to limit the use of simulation in construction. A common recommendation for removing this obstacle found in the literature leans towards developing simulation tools that reduce model development and experimentation time on the construction engineer’s side by packaging most of the knowledge required into the tool itself. Such “intelligent” simulation modeling tools may significantly impact the way construction engineers use simulation techniques in day-to-day decision?making. This paper presents a framework that extends and formalizes this recommendation by providing the foundation for building intelligence into simulation objects. The proposed framework provides the structure necessary for building intelligence and autonomy into simulation objects and permits a further reduction in the knowledge required to experiment with simulation models. This approach also automates model modification, not only through changes in numeric parameters, but through topological model changes as well, which may assist the model user in making many decisions throughout the different phases of simulation experimentation.     

Fuzzy Waste Load Allocation Model: Simulation-Optimization Approach

The problem of waste load allocation (WLA) for water quality management of a river system is addressed with a simulation-optimization approach. The WLA model developed in the study provides the best compromise solutions to the pollution control agency (PCA) responsible for maintaining the water quality and the dischargers disposing pollutants into the river system. A previously developed fuzzy waste load allocation model (FWLAM) is extended to incorporate QUAL2E, a water quality simulation model developed by the U.S. Environmental Protection Agency for modeling the pollutant transport in a river. The imprecision associated with establishing water quality standards and the aspirations of the PCA and dischargers are quantified using fuzzy goals with appropriate membership functions. The membership functions of the fuzzy goals represent the variation of the goal satisfaction in the system. A genetic algorithim (GA) is used as an optimization tool to find optimal fraction removal levels to the dischargers and the corresponding satisfaction level. Because a GA is an unconstrained optimization tool, it is extended to handle constraints by complementing it with homomorphous mapping (HM), a constraint handling method for evolutionary algorithms. The GA directs the decision vector in an encoded form to HM. HM, after a few interactions with QUAL2E, redirects the decoded solution back to the GA. The GA assigns a fitness value to the feasible solution vector and applies operators to refine the solution. This interaction among the GA, HM, and QUAL2E continues until a prespecified criterion for global optimality is met. Application of the model is illustrated with a case study of the Tunga-Bhadra River in South India.     

Optimal Construction Site Layout Considering Safety and Environmental Aspects

A good site layout is vital to ensure the safety of the working environment and effective and efficient operations. Site layout planning has significant impacts on productivity, costs, and duration of construction. Construction site layout planning involves identifying, sizing, and positioning temporary and permanent facilities within the boundary of the construction site. Site layout planning can be viewed as a complex optimization problem. Although construction site layout planning is a critical process, systematical analysis of this problem is always difficult because of the existence of a vast number of trades and interrelated planning constraints. The problem has been solved using two distinct approaches: Optimization techniques and heuristics methods. Mathematical optimization procedures have been developed to produce optimal solutions, but they are only applicable for small-size problems. Artificial intelligent techniques have been used practically to handle real-life problems. On the other hand, heuristic methods have been used to produce good but not optimal solutions for large problems. In this paper, an optimization model has been developed for solving the site layout planning problem considering safety and environmental issues and actual distance between facilities. Genetic algorithms are used as an optimization bed for the developed model. In order to validate the performance of the developed model, a real-life construction project was tested. The obtained results proved that satisfactory solutions were obtained.     

Multiscale Computation for Nano/Micromaterials

This paper presents a multiscale field theory and its applications in modeling and simulation of atomistic systems. The theoretical construction of the multiscale field theory is briefly introduced. A single crystal is discretized into finite-element mesh as if it is a continuous medium. However, each node is a representative unit cell, which contains a specified number of distinctive atoms. Ordinary differential equations for each atom in all nodes are obtained. Material behaviors of a given atomistic system at nano/microscale, subject to the combination of mechanical loadings, electromagnetic field, and temperature field, can be obtained through numerical simulations. Sample problems on wave propagation and simple tension have been solved to demonstrate the advantage and applicability of this multiscale field theory.     

Development and Application of an Integrated Optimization-Simulation Model for Major Irrigation Projects

A nonlinear, constrained multivariable optimization routine is developed for deciding the optimal canal water release and linked to a canal hydraulic module (MIKE 11) and command hydrological module (MIKE SHE). The optimization routine is solved using the sequential quadratic programming (SQP) technique. The hydraulic and the hydrological modules are calibrated and validated independently, and the results are found to be satisfactory. The integrated optimization-simulation model is applied to the Right Bank Main Canal System of Kangsabati Irrigation Project, West Bengal, India. An improved rotational delivery schedule based on long-term field data analysis is also developed. Three simulation scenarios are considered. These are (1) MIKE 11 and MIKE SHE simulation, (3) integrated optimization simulation, and (3) integrated optimization-simulation with improved schedule. Simulations were performed for Kharif (rainy) irrigation periods for 3 different years (1995–1997). The intercomparison of the three simulation scenarios showed that the application of the integrated optimization-simulation model reduced the gap between irrigation water supply and crop water demand and improved the spatial distribution of supply, thereby, minimizing the tail-end deprivation.     

Simulated Annealing–Genetic Algorithm for Transit Network Optimization

This paper presents a mathematical stochastic methodology for transit route network optimization. The goal is to provide an effective computational tool for the optimization of a large-scale transit route network to minimize transfers with reasonable route directness while maximizing service coverage. The methodology includes representation of transit route network solution search spaces, representation of transit route and network constraints, and a stochastic search scheme based on an integrated simulated annealing and genetic algorithm solution search method. The methodology has been implemented as a computer program, tested using previously published results, and applied to a large-scale realistic network optimization problem.     

Distributed Mass Damper System for Integrating Structural and Environmental Controls in Buildings

The writers recently proposed a new type of mass damper system to integrate structural and environmental control systems for buildings. External shading fins are used as mass dampers such that they can (1) control building energy consumption by adjusting the fins and, thus, the amount of sunlight entering the building; and (2) control structural movements by dissipating energy with the dampers during strong motions. Because shading fins are placed along the height of the building, the mass dampers are distributed along the building height instead of concentrated in one or a few locations like traditional tuned mass dampers (TMDs). The distributed mass damper (DMD) system is formulated and simulated for earthquake motions. Optimization is performed on damper parameters (i.e., masses, stiffness, and damping coefficients) of the passive DMD system to minimize structural responses. A near-optimal DMD system outperforms a single TMD system. The movable shading fins are also briefly discussed; they show a substantial savings in building energy consumption.     

Comparison of Gradually Varied Flow Computation Algorithms for Open-Channel Network

This paper presents a comparison of two algorithms—the forward-elimination and branch-segment transformation equations—for separating out end-node variables for each branch to model both steady and unsteady flows in branched and looped canal networks. In addition, the performance of the recursive forward-elimination method is compared with the standard forward-elimination method. The Saint–Venant equations are discretized using the four-point implicit Preissmann scheme, and the resulting nonlinear system of equations is solved using the Newton–Raphson method. The algorithm using branch-segment transformation equations is found to be at least five times faster than the algorithm using the forward-elimination method. Further, the algorithm using branch-segment transformation equations requires less computer storage than the algorithm using the forward-elimination method, particularly when only nonzero elements of the global matrix are stored. Comparison between the Gauss-elimination method and the sparse matrix solution technique for the solution of the global matrix revealed that the sparse matrix solution technique takes less computational time than the Gauss-elimination method.     

Optimal Design in the Presence of Modeling Uncertainties

The integration of modeling and simulation tools with robust and efficient methods of optimal design offers a rational approach to explore new concepts and designs. However, a widespread adaptation of these tools in the industry design environment will require that they incorporate a systematic analysis of uncertainty in all aspects of the design process. A lack of confidence in designs generated in a simulation-based approach is the result of uncertainties in the predictive capabilities of physics-based models used in the simulations, and poor representation of uncertainties and their propagation in a coupled systems engineering design problem. A data- and knowledge-lean environment, typical of a design process involving novel concepts, further exacerbates the situation; design engineers often make gross assumptions about distributional information of random variables and parameters, thereby adding to the uncertainty associated with the design results. The paper focuses on numerical and analytical tools by which to model uncertainty and risk in a simulation-based design environment, including cases where the uncertainty does not conform to standard probabilistic distributions. A specific focus of the modeling effort is an approach to establish confidence intervals for response predictions available from analytical and numerical models, as well as surrogate approximations used in the design process. Innovative adaptations of formal optimization methods in a nondeterministic design setting are discussed, including design problem formulations that examine the nondeterministic design problem in a multicriteria optimization framework. Simple design problems are used to illustrate the concepts and to underscore the deficiencies in a purely deterministic approach to the design problem.     

Refinement Indicator for Mesh Adaption in Shallow-Water Modeling

Automatic mesh refinement can create suitable resolution for a hydrodynamic simulation in a computationally efficient manner. Development of an automatic adaptive procedure will rely on estimating and/or controlling computational error by adapting the mesh parameters with respect to a particular measurement. Since a primary source of error in a discrete approximation of the shallow-water equations is inadequate mesh resolution, an adaptive mesh can be an efficient approach to increase accuracy. This paper introduces a simple indicator for the shallow water equations that measures the error in a norm of mass conservation to determine which elements require refinement or coarsening. The resulting adaptive grid gives results comparable to a much higher resolution (uniformly refined) mesh with less computational expense.     

Application Framework for Mapping and Simulation of Waste Handling Processes in Construction

This research is focused on modeling waste-handling processes in construction, with particular emphasis on how to map out and simulate on-site waste sorting processes. The research proposes an application framework for (1) guiding the development of process mapping models and simulation models; and (2) further assessing the cost effectiveness of on-site waste sorting efforts under practical site constraints (such as labor resource availability, time control on refuse chute usage, and limited working area space in a building site). The connection has been established between the mapping and simulation techniques in the context of modeling waste handling processes in construction sites, such that the process flowchart resulting from the mapping technique can serve as convenient model input to facilitate the creation of a “dynamic” operations simulation model. A case study of the on-site waste sorting method with one refuse chute for waste classification is presented to demonstrate the complete application framework spanning (1) process mapping; (2) mapping-to-simulation model conversion; and (3) method optimization based on valid simulations.     

Interactive Visualization for Evolutionary Optimization of Conceptual Rainfall-Streamflow Models

Calibration is an essential part of the application of conceptual rainfall-streamflow models to watershed management problems for civil engineers. However, the identification of a unique set of parameters is difficult, if not impossible, for commonly used models. Most multiobjective methods and uncertainty assessment tools require substantial numbers of function evaluations and limit the intervention of experienced modelers in the calibration process. This paper demonstrates the application of an efficient user-driven calibration-support system to conceptual rainfall-streamflow models. The system is designed to assist the hydrological modeler by means of rapid sampling of solutions, clustering, and visualization, together with interactivity to exploit the expertise of the user and/or the knowledge revealed by the clustering technique. The efficiency of the multiobjective calibration is enhanced through the use of a novel objective function based on hydrograph slope. The application of the system to the calibration of the SIXPAR conceptual rainfall-streamflow model using a synthetic time series is shown to be effective.     

Efficient Parallel Implementation of Hybrid Optimization Approaches for Solving Groundwater Inverse Problems

Inverse problems that are constrained by large-scale partial differential equation (PDE) systems demand very large computational resources. Solutions to these problems generally require the solution of a large number of complex PDE systems. Three-dimensional groundwater inverse problems fall under this category. In this paper, we describe the implementation of a parallel simulation-optimization framework for solving PDE-based inverse problems and demonstrate it for the solution of groundwater contaminant source release history reconstruction problems that are of practical importance. The optimization component employs several optimization algorithms, including genetic algorithms (GAs) and several local search (LS) approaches that can be used in a hybrid mode. This hybrid GA-LS optimizer is used to drive a parallel finite-element (FEM) groundwater forward transport simulator. Parallelism is exploited within the transport simulator (fine grained parallelism) as well as the optimizer (coarse grained parallelism) through the exclusive use of the Message Passing Interface (MPI) communication library. Algorithmic and parallel performance results are presented for an IBM SP3 supercomputer. Simulation and performance results presented in this paper illustrate that an effective combination of efficient optimization algorithms and parallel computing can enable solution to three-dimensional groundwater inverse problems of a size and complexity not attempted before.     

Optimization of Open-Pit Mine Depressurization System Using Simulated Annealing Technique

Opencast mines operating in an area with dominant groundwater features may face hydrology-related problems such as heaving and bursting of the mine floor due to excessive uplift pressure. A proper groundwater control system has to be implemented to solve these problems. But the groundwater control system, which includes dewatering and depressurization wells may also create impacts on local groundwater flows. Therefore, an optimization-based development of the groundwater control system is required to ensure that local and regional hydrogeological impacts are within acceptable limits. This note presents a case study where an optimization program based on the simulated annealing technique was developed and applied to a three-dimensional seven-layer groundwater model. The calibrated groundwater flow model, which is based on MODFLOW, was used as the simulation component in the linked simulation-optimization model. The combined model was then used to identify the optimum depressurization strategy. The results show that this combined simulation and optimization methodology is a viable approach for solving large-scale groundwater management problems.     

Efficient Spreadsheet Algorithm for First-Order Reliability Method

A new spreadsheet-cell-object-oriented algorithm for the first-order reliability method is proposed and illustrated for cases with correlated nonnormals and explicit and implicit performance functions. The new approach differs from the writers earlier algorithm by obviating the need for computations of equivalent normal means and equivalent normal standard deviations. It obtains the solution faster and is more efficient, robust, and succinct. Other advantages include ease of initialization prior to constrained optimization, ease of randomization of initial values for checking robustness, and fewer required optimization constraints during spreadsheet-automated search for the design point. Two cases with implicit performance functions, namely an asymmetrically loaded beam on Winkler medium and a strut with complex supports are analyzed using the new approach and discussed. Comparisons are also made between the proposed approach and that based on Rosenblatt transformation.     

Rotorcraft Trajectory Optimization with Realizability Considerations

This paper considers the problem of computing optimal trajectories for rotorcraft systems. The vehicle is described through a flight mechanics model, and the optimal control problem is solved by discretizing the vehicle governing equations using a finite-element method, followed by optimization of the resulting finite-dimensional problem. It is found that the computed control policies exhibit oscillations and very high—and therefore unrealistic—time rates, especially for aggressive or emergency maneuvers. Highly oscillatory controls can affect the vehicle trajectory by, for example, exciting short period type oscillations. We argue that this behavior of the computed controls is due to the lack of modeling detail of the vehicle actuators, implied by the classical treatment of the system controls as algebraic variables. We propose a simple, low-cost solution that is based on the recovery of the control time rates through a Galerkin projection. This approach is motivated by the desire to avoid direct modeling of the actuator dynamics, which typically requires one to resolve fine temporal scales in the solution. The recovered control rates can then be constrained to remain within physically acceptable bounds during the solution and can also be included in the optimization cost functions. Numerical experiments are shown to demonstrate that smoother control time histories and vehicle trajectories are computed through this approach.     

Simplified Method for the Characterization of the Hydrograph following a Sudden Partial Dam Break

This paper presents a simplified approach to the characterization of the hydrograph following the partial collapse of concrete gravity dams. The proposed approach uses a simplified representation of the reservoir geometry and is based on the numerical solution of shallow water equations to study the two-dimensional evolution of the water surface within the reservoir. The numerical results are made dimensionless and reorganized so as to compute the peak discharge, the duration and the recession limb of the dam break hydrograph. The proposed practical approach provides a quite satisfactory reproduction of the computed hydrograph for a wide set of realistic situations that have been simulated in detail.     

Object-oriented Simulation Model for Earthmoving Operations

This paper presents a simulation engine, developed to model earthmoving operations. The engine has been designed utilizing object-oriented features, and it represents a main component in a newly developed automated system for selecting a near-optimum fleet configuration. It provides contractors with a vehicle for estimating the time and cost of this class of projects considering different practical scenarios. The system has been implemented in a Microsoft environment to facilitate integration among its components, which have been developed in the same environment. The paper focuses on the modeling aspects of the simulation process using discrete event simulation and object orientation. A numerical example of an actual case is analyzed to validate the developed simulation engine and demonstrate its capabilities. The results are compared to those generated using Caterpillar software (FPC). The engine and FPC recommended the same fleet and their estimated project durations were very close, with a difference less than 8%. Unlike FPC, the developed engine, however, can model and account for uncertainty during the execution of earthmoving operations in a reliable manner.     

Framework for Providing Customized Data Representations for Effective and Efficient Interaction with Mobile Computing Solutions on Construction Sites

Conceptual representations of information contained in product and process models are often difficult to use for accessing data when performing engineering tasks. This is especially true if project-management information contained in product and process models needs to be made accessible on a mobile computer on construction sites. To make this information accessible, customized conceptual and visual information representations are needed. For the project-management tasks of progress monitoring and creating and administering punch lists, existing approaches that provide access to relevant project information are ineffective and inefficient in transforming information from product and process models into usable representations. As a result, these applications do not always provide information representations that are of the required structure, granularity, and type. In this paper, we describe a navigational model framework, which is an approach that effectively and efficiently creates and manages different views of information contained in product and process models. We validated this framework by implementing a prototype system and testing it through a designed set of experiments. The use cases for these experiments were established in an extensive study on the information and data collection needs on construction sites.