Construction Resource Allocation Using a Genetic Algorithm

A proper allocation of limited resources (men, machines, materials, and money) is critical in a construction project. Traditionally, resource allocation problems have been solved using methods in operations research (OR), such as mathematical programming. In recent years, genetic algorithms (GA) have emerged as an effective optimization methodology. One major advantage of the GA approach over the OR approach is that the GA approach is universal for various types of optimization problems, unlike the OR approach which varies depending on the types of problems at hand. This paper shows an application of GA to a resource allocation problem in the construction industry in which a contractor tries to maximize profit by properly allocating various pieces of heavy equipment to various ongoing construction projects. This type of problem has customarily been solved by the linear programming method. GA has proved to be quite an attractive alternate to the OR method. Since the GA method is more universal than the OR method, the program can be easily modified to solve other types of problems. A description of a computer program written in Visual Basic is also presented

ANN: A Set of Educational Neural Net Simulators

ANN has been developed on MS-DOS computers primarily for educational uses. Currently, it consists of six simulation programs. ANN1 is a very simple neural net which shows how a network learns by adjusting its connection weights. ANN2 is a single processing element neural net, in which the user trains the network manually by adjusting the connection weights and the threshold value. ANN3 is a manually trained simple two layered network. It demonstrates the power of hidden neurons. ANN4is a Bidirectional Associative Memory network. ANN5 is a Perceptron that learns from examples. ANN6 is a network based on the backpropagation of error. Graphics have been used extensively in all networks. Students can observe the way these networks learn. Hypertext is used to explain concepts, and also serves as an online user\u27s manual

Educational Software Development Using Hypertext and Expert System Software Concepts

This paper presents two computer software concepts: hypertext and expert systems; which are useful for educational software development. Good educational software enhances the learning process and offers opportunities for faculty to provide additional materials for independent studies, which would otherwise be impossible, due to the limited time and incredible growing rate of technological progress. The hypertext concept offers the students a non-linear learning style, while the expert system concept provides explanation facilities for students to probe the logic of the systems. Both approaches are extremely useful for educational software. With the proper tools, the courseware can be implemented easily and rapidly. Educational software packages in the area of concrete technology have been currently developed for use in the Department of Civil Engineering at Christian Brothers University. They are utilized as examples. Development tools, KnowledgePro and CBC-Xpert, are also discussed

Concrete Beam Design Optimization with Genetic Algorithms

This paper demonstrates an application of the natural selection process to the design of structural members. Reinforced concrete beam design is used as the example to show how various chromosomes representing a design solution can be formulated. Fitter chromosomes (or better solutions) have a better chance of being selected for cross over; this in turn creates better generations. Random mutation is used to enhance the diversity of the population. The evolution progresses through several generations, and the best solution is then used in the design. The method gives reasonable results, but sometimes a local (as opposed to the global) optimized solution is obtained

JAVA Applets for teaching neural networks

Five Java applets were developed for use with an undergraduate course on neural networks at Christian Brothers University. The paper starts with an overview of the course and a list of the hands-on software utilized. Some Java considerations are discussed; despite its weak points, Java was chosen for ease of Web delivery and platform independency. Detailed descriptions of the five Java applets follow, including implementation specifics and brief overviews of the underlying algorithms and theory. The five applets are as follows: SINGLE, a simulation of a simple single-layer network; DELTA, a demonstration of the delta rule learning algorithm; HOPFIELD, an application of a Hopfield network towards pattern recognition; BAM, an application of a bidirectional associative memory network towards pattern recognition and association; and CUMSEL, a demonstration of the cumulative selection process. The applets are also posted on the Web for public usage at http://www.cbu.edu/-pong/ai/

Estimating Tote Drop Height & Impact Acceleration from a Transportation Recorder

Abstract: Healthcare products are often shipped from a distribution center to a retail store in plastic totes. Previous research on plastic tote distribution at the Healthcare Packaging Consortium has suggested that the use of bubble wrap and air pillows at tote bottom and top, respectively, reduces impact. In addition to the use of bubble wrap and air pillows, monitoring tote handling would also have the potential to reduce distribution damages. A transportation recorder was used to aid this study. Equations were developed from the drop test data to estimate drop height and impact acceleration at tote bottom from impact acceleration obtained from the recorder