Binary Resource Leveling Model: Activity Splitting Allowed

The resource leveling problem is common and has been studied numerous times. In these studies and the resulting solutions, there exists a common element, which is once an activity is started, it cannot be stopped and restarted again. That is, it cannot be split. In many instances in actual construction, there exist activities that can be stopped and restarted. However, not all activities have this characteristic. This paper presents a linear program binary variable model to level resources that permits selected activities to stop and restart. This splitting of activities results in improvement to the leveling solution that is traditionally achieved when splitting is not permitted. Examples are presented that illustrate the improvement in the solution obtained from the proposed model compared to models that do not allow splitting and compares the result to that obtained using commercially available software. The results are beneficial to construction professionals who may be unaware of the impacts of using activity splitting.     

Global Optimization of Combined Region Aggregation and Leveling Model

The advent and widespread use of innovative spatial analysis technologies, such as geographic information systems (GIS), computer aided design (CAD) systems, and global positioning systems (GPS), have prompted great interest in spatial optimization. The tasks of selecting an optimal subregion from a larger region—region aggregation—and determining an optimal strategy for cutting and filling that subregion to a uniform elevation—land leveling—are examples of spatial analyses that can benefit from these powerful computer technologies. The combined region aggregation and leveling problem is a complex spatial problem that often involves the comprehensive consideration of multiple, incommensurate, and often conflicting objectives, while at the same time satisfying a set of prespecified physical and logical constraints. Traditionally, these two problems are solved separately, often precluding the identification of global optima. Through this research, a multiobjective integer programming model that considers these problems simultaneously is formulated, a computational algorithm for solving the model is presented, and numerical results that demonstrate the efficiency and effectiveness of this procedure are discussed. Computational experiments report polynomial complexity of the heuristic procedure against exponential worst-case complexity of traditional enumerative methods.     

Flexible Model for Time/Cost Tradeoff Problem

This paper presents a flexible mixed integer-programming model for the solution of the time/cost tradeoff problem encountered in project management. Whereas it is commonly assumed that the time/cost function is linear, the model presented in this paper makes minimal assumptions and accommodates any type of cost function that is linear, piecewise linear or discrete. The model can be used for answering various “what if” questions that may be very helpful to a project manager in making rational decisions. The basic model minimizes the total cost which is the sum of direct and indirect costs, subject to a project deadline constraint. A simple modification in the model changes the focus from minimizing total cost to minimizing project completion time subject to a resource constraint. The models of this paper can be set up and run very easily on commercially available optimization packages with an integer-programming module. These models provide a viable alternative to more specialized algorithms developed for the time/cost tradeoff problem simply because a typical project manager may not have the necessary skills or resources to implement specialized algorithms.     

Optimization Model for Aggregate Blending

An optimization model of aggregate blending is presented that considers gradation, cost, and design requirements and is applicable to the blending of any number of aggregates. The model is formulated as a quadratic programming problem that minimizes the mean deviation from midpoint specification limits, subject to constraints on the preceding requirements. The model is applied to a numerical aggregate blending problem. Sensitivity analysis is performed to show how the model can also be used to minimize cost or to provide a trade‐off between mean deviation and cost. Extensions of the model to accommodate special practical cases are examined.     

Balancing Connectivity of Deteriorating Bridge Networks and Long-Term Maintenance Cost through Optimization

Due to aggressive environmental stressors and increasing traffic loads, highway bridges are undergoing significant deterioration in both condition and safety. Timely and adequate maintenance interventions are therefore crucial to ensure the functionality of existing bridges in a network. Under budget constraints, it is important to prioritize maintenance needs to bridges that are most significant to the functionality of the entire network. In this paper, the network-level bridge maintenance planning problem is posed as a combinatorial optimization and is automated by a genetic algorithm (GA) to select and allocate maintenance interventions of different types among networked bridges as well as over a specified time horizon. Two conflicting objective functions are considered simultaneously: (1) The overall performance of a bridge network expressed by the time-dependent reliability of connectivity between the origin and the destination locations and (2) the present value of total maintenance cost over the specified time horizon. A variety of maintenance types, which differ in unit costs as well as in effects on bridge performance in terms of improvement in structural reliability levels, are used in the optimization. An event tree analysis is carried out to obtain a closed-form expression for the network connectivity reliability. As an illustration example, the GA-based procedure is applied to deteriorating deck slabs of an existing 13-bridge network located in Colorado. It is shown that the proposed maintenance planning procedure has the capability of both prioritizing scarce maintenance needs to deteriorating bridges that are most crucial to the network performance and cost-effectively distributing maintenance interventions over the time horizon.     

New Mathematical Optimization Model for Construction Site Layout

Layout of temporary construction facilities (objects) is an important activity during the planning process of construction projects. The construction area layout is a complex problem whose solution requires the use of analytical models. Existing popular models employ genetic algorithms that have proven to be useful tools in generating near optimal site layouts. This paper presents an alternative approach based on mathematical optimization that offers several important features and generates a global optimal solution. The construction area consists of an unavailable area that includes existing facilities (sites) and available area in which the objects can be located. The available area is divided into regions that are formulated using binary variables. The locations of the objects are determined by optimizing an objective function subject to a variety of physical and functional constraints. The objective function minimizes the total weighted distance between the objects and the sites as well as among the objects (if desired). The distance can be expressed as Euclidean or Manhattan distance. Constraints that ensure objects do not overlap are developed. The new approach, which considers a continuous space in locating the objects simultaneously, offers such capabilities as accommodating object adjacency constraints, facility proximity constraints, object–region constraints, flexible orientation of objects, visibility constraints, and nonrectangular objects, regions, and construction areas. Application of the model is illustrated using two examples involving single and multiple objects. The proposed model is efficient and easy to apply, and as such should be of interest to construction engineers and practitioners.     

Business Strategy and Capital Allocation Optimization Model for Practitioners

Construction engineering companies usually provide a variety of services. To be competitive, companies have to organize their operations strategically based on market demands within the limitations of their own resources. Optimization of these resources is of vital importance for these companies. Historically, decisions on resource allocations to various construction market segments were made exclusively based on intuitive judgment. In previous literature, the proposed models on capital allocation place emphasis on formulating cash-flow forecasting and planning strategy on project level. However, existing technologies and established mathematical methods provide a sound base for quantitative analysis on company-level business strategy and capital allocation. This note proposes a linear programming model that can be conveniently applied by construction practitioners. The model incorporates the project cost structure and considers the business constraints such as bonding capacity and borrowing capital capacity. Its objective is to achieve maximum profit through improving project management efficiency and setting appropriate sales goals in various market segments based on market demands. It is a decision-making tool that provides “what-if” analysis. The solutions and alternatives of the model give the decision makers an excellent insight for making the best choice.     

Parameter Optimization and Sensitivity Analysis for Disturbed State Constitutive Model

Constitutive models for geologic materials and interfaces involve a number of parameters that need to be determined from appropriate laboratory tests. Because the test behavior is influenced by a number of factors such as material variability in test specimens, initial density, mean pressure, and stress paths, the parameters determined from such tests need to be averaged or optimized. The averaging procedure is often used. However, in view of the importance of the parameters in analysis and design, it is desirable and necessary to use advanced procedures such as optimization methods so as to find their improved and realistic values. This paper presents an optimization procedure for the determination of parameters in the unified disturbed state concept constitutive models. A series of multiaxial laboratory tests on a sand under different initial mean pressures, density, and stress paths are used to evaluate the optimized parameters. The stress-strain and volume change behavior is then back-predicted using the parameters from the conventional averaging procedure and the proposed optimization procedure. The results show that the optimized parameters provide improved predictions of the test data. The optimized parameters are used in a finite element procedure to predict cyclic behavior in a boundary value problem involving a shake table test. The proposed procedure can provide a useful methodology for the optimization of parameters for a wide range of available (plasticity, creep, damage, etc.) constitutive models. It can lead to improved analysis and design of geotechnical problems, particularly while using computer (finite element) procedures.     

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.     

Determining Attribute Weights in a CBR Model for Early Cost Prediction of Structural Systems

This paper compares the performance of three optimization techniques, namely feature counting, gradient descent, and genetic algorithms (GA) in generating attribute weights that were used in a spreadsheet-based case based reasoning (CBR) prediction model. The generation of the attribute weights by using the three optimization techniques and the development of the procedure used in the CBR model are described in this paper in detail. The model was tested by using data pertaining to the early design parameters and unit cost of the structural system of 29 residential building projects. The results indicated that GA-augmented CBR performed better than CBR used in association with the other two optimization techniques. The study is of benefit primarily to researchers as it compares the impact attribute weights generated by three different optimization techniques on the performance of a CBR prediction tool.     

Fuzzy Optimization Model for Earthwork Allocations with Imprecise Parameters

Existing linear programming (LP) models of earthwork allocations in roadway construction assume that unit cost coefficients of earthwork activities and borrow pits/disposal sites capacities are certain and deterministic numbers. However in real-world problems there are naturally some uncertainties inherited in these values, which make it difficult to represent a single value as the candidate of entire possible values. This paper presents a fuzzy linear programming (FLP) model of earthwork allocations based on the fact of assuming unit cost coefficients and borrow pits/disposal sites capacities as fuzzy numbers while minimizing total earth-moving cost as an objective function. A method based on α cuts of a fuzzy set is used to take the uncertainty in borrow pits/disposal sites capacities into account. The uncertainty in fuzzy cost coefficients of the objective function and its effects on decision variables of the earthwork allocations model are also considered using the method presented by Chanas and Kuchta in 1994. Subsequently, a more general model is suggested which considers both uncertainties in borrow pits/disposal sites capacities and cost coefficients simultaneously. It is demonstrated that the presented FLP, compared to a deterministic LP, introduces a more robust solution; as the result of giving fuzziness to the uncertain parameters. Such a solution could be beneficial in real world decision making where uncertainties on resources and activities cost exist.     

Using the Cumulative Cost Model to Forecast Equipment Repair Costs: Two Different Methodologies

Professionals in the construction industry must be able to accurately forecast costs. Doing so not only helps assure reasonable profits for companies, but it can also help ensure that projects are delivered within budget for clients. Forecasting of equipment repair costs is one element of the larger problem of predicting overall costs. The cumulative cost model can provide construction engineers with a valuable tool for better understanding the nature of repair costs as they relate to production fleets. Data that are being collected (or that could be collected) can assist in the determination of the rate of accumulation of repair costs for a machine for a given period of use or the estimation of fleet repair budgets for a job or period. There are two different methodologies for constructing the repair cost portion of the cumulative cost model: life-to-date (LTD) repair costs and the period-cost-based (PCB) model. This paper will provide the steps and background for each of these two methodologies and compare them using a practical example.     

Optimization Model for Allocating Water in a River Basin during a Drought

Optimization of water use is a complex problem in a large scale river basin. One of the most important approaches in optimizing water use in a river basin is to find the relationship between water demand and water supply. The parameters that affect demand, supply, and the methods of evaluation of such elements are discussed in this study. Also, a method is presented for providing objective and constraint functions from considering these effects. Fuzzy logic theory is used to modify the stochastic dynamic programming (SDP) method such that an optimization model is developed for allocating water and can be defined as the “stochastic fuzzy dynamic programming (SFDP)” method. This method is applied to optimize water use in the Kor and Seevand river basins, located in the Bakhtegan watershed, Fars, Iran. The primary water resources management consisted of the variability ranges of decision variables such as release from Doroodzan Dam and reservoir storage and was also used for allocating water in these river basins based on the SDP method. Therefore, in the present study, these variability ranges are obtained based on historical data, and divided into several record classes. Optimum class of release, a case of the record classes, was obtained from the optimization model for each month during the past 4 out of 25 years. Although, the SFDP method can be used in optimizing water allocation during each period, the method is structured and discussed only during the drought periods (4 years). Later, a comparison was made between optimum classes and record classes that were operated during the primary water resources management. During this period, the SFDP method reduced the difference between the release from the dam and the total water demand of the river basin. Therefore, approximately a 27% improvement in adaptation between release and demand could be attained. Finally, if the decision maker makes the decision for the release from the dam that is optimal according to our objective function, the reliability of reservoir operating can be increased by 51% during future droughts.     

Analytical Model for FRP Confinement of Concrete Columns with and without Internal Steel Reinforcement

The paper aims to contribute to a better understanding of the behavior of reinforced concrete columns confined with fiber-reinforced polymer (FRP) sheets. In particular, some new insights on interaction mechanisms between internal steel reinforcement and external FRP strengthening and their influence on efficiency of FRP confinement technique are given. In this context a procedure to generate the complete stress-strain response including new analytical proposals for (1) effective confinement pressure at failure; (2) peak stress; (3) ultimate stress; (4) ultimate axial strain; and (5) axial strain corresponding to peak stress for FRP confined elements with circular and rectangular cross sections, with and without internal steel reinforcement, is presented. Interaction mechanisms between internal steel reinforcement and external FRP strengthening, shown by some experimental results obtained at the University of Padova with accurate measurements, are taken into account in the analytical model. Four experimental databases regarding FRP confined concrete columns, with circular and rectangular cross section with and without steel reinforcement, are gathered for the assessment of some of the confinement models shown in literature and the new proposed model. The proposed model shows a good performance and analytical stress-strain curves approximate some available test results quite well.     

Using Decision Trees for Determining Attribute Weights in a Case-Based Model of Early Cost Prediction

This paper compares the performance of three different decision-tree-based methods of assigning attribute weights to be used in a case-based reasoning (CBR) prediction model. The generation of the attribute weights is performed by considering the presence, absence, and the positions of the attributes in the decision tree. This process and the development of the CBR simulation model are described in the paper. The model was tested by using data pertaining to the early design parameters and unit cost of the structural system of residential building projects. The CBR results indicate that the attribute weights generated by taking into account the information gain of all the attributes performed better than the attribute weights generated by considering only the appearance of attributes in the tree. The study is of benefit primarily to researchers, as it compares the impact of attribute weights generated by three different methods and, hence, highlights the fact that the prediction rate of models such as CBR largely depends on the data associated with the parameters used in the model.     

Calibration of Soil Constitutive Models with Spatially Varying Parameters

Soil constitutive models are frequently calibrated from laboratory tests that utilize global boundary measurements, which necessarily relegate soil response to that of a homogenized equivalent medium. This paper demonstrates the applicability of advanced experimental technologies to enhance the state of model-based predictions in soil mechanics by taking into account the possibility of material heterogeneity during model calibration. By utilizing the full-field displacement measurement technique of three-dimensional digital image correlation, displacements of the surfaces of deforming triaxial sand specimens are measured throughout deformation. These displacements are assimilated into finite-element (FE) models of the test specimen through solution of an inverse problem. During optimization, in which the difference between measured and predicted displacements across the specimen surface form the basis for the objective function, model parameters are allowed to vary spatially throughout the specimen volume. FE models allowing three different levels of spatial variability are tested. Results indicate that accommodating consideration of material heterogeneity during calibration leads to more accurate predictions of global stress-strain behavior that are more faithful to observed full-field response.     

Optimization of Project Time-Cost Trade-Off Problem with Discounted Cash Flows

Traditional time-cost trade-off (TCTO) analysis assumes constant value of activities’ cost along the project time span. However, the value of money decreases with time and, therefore, discounted cash flows should be considered when solving TCTO optimization problem. Optimization problems in project management have been traditionally solved by two distinctive approaches: heuristic methods and optimization techniques. Although heuristic methods can handle large-size projects, they do not guarantee optimal solutions. A nonlinear mathematical optimization model for project TCTO problem is developed, which minimizes project direct cost and takes into account discounted cash flows. Costs of activities are assumed to be incurred at their finish times. The model guarantees the optimal solution, in which precise discrete activity time-cost function is used. The model input includes precedence relationship between project activities, discrete utility data for project activities, and discount rate. Details of model formulation are illustrated by an example project. The results show that selected activities’ durations and costs and consequently optimal project duration differ from traditional analysis if discounted cash flow is considered. The new approach provides project practitioners with a way for considering net present value in time-cost decisions so that the best option can be identified.     

Time-Cost Optimization of Construction Projects with Generalized Activity Constraints

Time-cost analysis is an important element of project scheduling, especially for lengthy and costly construction projects, as it evaluates alternative schedules and establishes an optimum one considering any project completion deadline. Existing methods for time-cost analysis have not adequately considered typical activity and project characteristics, such as generalized precedence relationships between activities, external time constraints, activity planning constraints, and bonuses/penalties for early/delayed project completion that would provide a more realistic representation of actual construction projects. The present work aims to incorporate such characteristics in the analysis and has developed two solution methods, an exact and an approximate one. The exact method utilizes a linear/integer programming model to provide the optimal project time-cost curve and the minimum cost schedule considering all activity time-cost alternatives together. The approximate method performs a progressive project length reduction providing a near-optimal project time-cost curve but it is faster than the exact method as it examines only certain activities at each stage. In addition, it can be easily incorporated in project scheduling software. Evaluation results indicate that both methods can effectively simulate the structure of construction projects, and their application is expected to provide time and cost savings.     

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.     

Analytical Model for Fracture Grouting in Sand

A conceptual, analytical model has been developed to describe the fracture grouting process in sand. The objective of the model is to improve understanding about this process in sand and to model propagation of the fractures. The results can be used to assess the parameters that control the fracture process. It is assumed that the complicated shape of a fracture in sand can be simplified to a geometrical shape (such as a tube or a plane) as a first approximation. Filtration of the grout appears to have a significant influence on the fracture shape when grout is injected into permeable subsoil such as sand. By assuming a pressure at which a fracture starts and a minimum pressure for propagation, it appeared possible to calculate the width-to-length ratio of the fracture independent of other soil properties. Quantification of the flow inside a fracture and the filtration processes resulted in a model that has been used to study differences in fracturing behavior in model tests and field tests on fracture grouting in sand. It was concluded that the width-to-length ratio of the fractures in a permeable soil decreases if the injection pressure of the grout or the permeability of the grout cake is decreased.