Building Research Skills: Course-Integrated Training Methods

An enriched syllabus has been experimentally introduced to undergraduate level students enrolled in a geotechnical engineering course. Two research assignments have been integrated in the course, which require students to find information on a geotechnical engineering topic using both print and electronic resources available at the university library and on the Internet. Aiming to foster the development of technical information literacy and communication skills, the students are required to prepare a report based on a specific set of guidelines, followed by oral presentations of the topics researched. Between the two assignments, a lecture on the subject of identifying and using information resources for geotechnical engineering is included in the syllabus. Preliminary results of the study are presented along with directions for designing and implementing an enriched curriculum for geotechnical engineering courses.     

Neural Network Embedded Monte Carlo Approach for Water Quality Modeling under Input Information Uncertainty

This paper proposes a neural network embedded Monte Carlo (NNMC) approach to account for uncertainty in water quality modeling. The framework of the proposed method has three major parts: a numerical water quality model, a neural network technique, and Monte Carlo simulation. The numerical model is used to generate desirable output for training and testing sets, and the neural network is used as a universal functional mapping tool to approximate the input-output response of the numerical model. The Monte Carlo simulation then uses the neural network to generate numerical realizations based on a probabilistic distribution of parameters, thus obtaining a probabilistic distribution of the simulated state variables. By embedding a neural network into the conventional Monte Carlo simulation, the proposed approach significantly improves upon the conventional method in computational efficiency. The proposed approach has been applied to uncertainty and risk analyses of a phosphorus model for Triadelphia Reservoir in Maryland. The results of this research show that the NNMC approach has potential for efficient uncertainty analysis of water quality modeling.     

Effectiveness of Learning Transportation Network Growth through Simulation

Computer simulation plays an increasingly important role in engineering education as a tool for enhancing classroom learning. This research investigates the efficacy of using simulation in teaching the topic of transportation network growth through an experiment conducted at the Civil Engineering Department of the University of Minnesota. In the experiment, a network growth simulator program (SONG) was incorporated into a senior/graduate class in transportation system analysis. Results of the experiment show that the use of SONG effectively enhanced students’ learning in terms of helping students develop in-depth understanding about the development process of network patterns, and helped them develop some aspects of judgment, problem-solving, and decision-making skills. However, the use of SONG may have been more effective had some other barriers to learning been overcome.     

Tank Simulation for the Optimization of Water Distribution Networks

In this paper an original approach to the simulation of floating-on-the-system tanks as decision variables for water distribution system design optimization is presented, aiming to bridge the gap between traditional engineering practice and mathematical considerations needed for genetic algorithms (GAs). The paper includes a systematic and detailed critical overview of various mathematical approaches in literature, as well as a novel, more “engineering oriented” approach to the simulation of tanks as decision variables for water distribution system design optimization, describing in detail assumptions and impacts to the evaluation of potential solutions. Tank simulation is based on two decision variables: capacity and minimum normal operational level, omitting risers. Shape and ratio between emergency/total capacities are taken into consideration as design parameters. Assessment of tank performance is carried out by four criteria for the normal daily operational cycle, differentiating between operational and filling capacity, as well as two further criteria for emergency flows. The original design and operational mathematical assumptions are implemented in a fuzzy multiobjective GA model, which is applied to the well-known example from literature “Anytown” water distribution network to benchmark the results.     

Gradually Varied Flow Computation in Cyclic Looped Channel Networks

A novel and computationally efficient algorithm is presented to compute the water surface profiles in steady, gradually varied flows of open channel networks. This algorithm allows calculation of flow depths and discharges at all sections of a cyclic looped open channel network. The algorithm is based on the principles of (1) classifying the computations in an individual channel as an initial value problem or a boundary value problem; (2) determining the path for linking the solutions from individual channels; and (3) an iterative Newton–Raphson technique for obtaining the network solution, starting from initial assumptions for discharges in as few channels as possible. The proposed algorithm is computationally more efficient than the presently available direct method by orders of magnitude because it does not involve costly inversions of large matrices in its formulation. The application of this algorithm is illustrated through an example network.     

Competent Genetic-Evolutionary Optimization of Water Distribution Systems

A genetic algorithm has been applied to the optimal design and rehabilitation of a water distribution system. Many of the previous applications have been limited to small water distribution systems, where the computer time used for solving the problem has been relatively small. In order to apply genetic and evolutionary optimization technique to a large-scale water distribution system, this paper employs one of competent genetic-evolutionary algorithms—a messy genetic algorithm to enhance the efficiency of an optimization procedure. A maximum flexibility is ensured by the formulation of a string and solution representation scheme, a fitness definition, and the integration of a well-developed hydraulic network solver that facilitate the application of a genetic algorithm to the optimization of a water distribution system. Two benchmark problems of water pipeline design and a real water distribution system are presented to demonstrate the application of the improved technique. The results obtained show that the number of the design trials required by the messy genetic algorithm is consistently fewer than the other genetic algorithms.     

Cost-Benefit Analysis of Embedded Sensor System for Construction Materials Tracking

Tracking and monitoring the location of materials on a construction job site is an important, yet commonly overlooked aspect of field data acquisition because timely information about the status of materials, equipment, tools, and labor resources are directly related to the successful completion of a project. With the advanced technologies and innovations in the construction industry, it has become technically viable to implement automated tracking for construction materials. Through the development of an embedded sensor system, this paper illustrates the implementation of pilot experiments examining the accuracy of a system’s performance. A cost-benefit analysis is conducted to illustrate labor savings associated with construction materials handling by comparison between manual and sensor-based materials tracking. The presented embedded sensor system can be extended into diverse application areas in tracking and monitoring framework by providing improved method of field data acquisition and information management.     

Optimization of Water Distribution Networks Using Integer Linear Programming

In this study optimum design of municipal water distribution networks for a single loading condition is determined by the branch and bound integer linear programming technique. The hydraulic and optimization analyses are linked through an iterative procedure. This procedure enables us to design a water distribution system that satisfies all required constraints with a minimum total cost. The constraints include pipe sizes, which are limited to the commercially available sizes, reservoir levels, pipe flow velocities, and nodal pressures. Accuracy of the developed model has been assessed using a network with limited solution alternatives, the optimal solution of which can be determined without employing optimization techniques. The proposed model has also been applied to a network solved by others. Comparison of the results indicates that the accuracy and convergence of the proposed method is quite satisfactory.     

Robust Water System Design with Commercial Intelligent Search Optimizers

Intelligent-search-based optimizers have shown promise in providing improved links between analysis and design. Genetic-algorithm-based optimizers are often used, but other heuristic methods, such as tabu search, have also been used with good results. A major impediment to using heuristic search methods has been the lack of user-friendly, commercially available software. This is no longer the case due to the availability of several commercial intelligent-search-based optimizers. Robust analysis and optimization of a water distribution network is demonstrated with four commercial optimizers. The hydraulic analysis is performed with WinPipes.EXE, a Windows program based on the EPANET source code. The method is demonstrated by optimizing the New York City Water Supply Tunnel problem, and by the optimal design of a 15-loop, Almos water distribution system. The method is robust because it uses reliable and efficient commercial optimizers, a popular pipe network solver, and constraint penalties to meet the multiple goals of a reliable, low-cost water distribution system, capable of meeting maximum hour demands and fire flow demands.     

Pressure-Driven Demand and Leakage Simulation for Water Distribution Networks

Increasingly, water loss via leakage is acknowledged as one of the main challenges facing water distribution system operations. The consideration of water loss over time, as systems age, physical networks grow, and consumption patterns mature, should form an integral part of effective asset management, rendering any simulation model capable of quantifying pressure-driven leakage indispensable. To this end, a novel steady-state network simulation model that fully integrates into a classical hydraulic representation, pressure-driven demand and leakage at the pipe level is developed and presented here. After presenting a brief literature review about leakage modeling, the importance of a more realistic simulation model allowing for leakage analysis is demonstrated. The algorithm is then tested from a numerical standpoint and subjected to a convergence analysis. These analyses are performed on a case study involving two networks derived from real systems. Experimentally observed convergence/error statistics demonstrate the high robustness of the proposed pressure-driven demand and leakage simulation model.     

Enhancing Civil Engineering Education and ABET Criteria through Practical Experience

The Accreditation Board for Engineering and Technology (ABET) has adopted a revised set of criteria for accrediting engineering programs. Nevertheless, as in the past, civil engineering departments will be required to demonstrate proficiency in specific subject areas which are included in the ABET program criteria. This paper investigates how civil engineering students at Lamar University improved their understanding of various subject areas required by ABET and listed in the Program Criteria for Civil and Similarly Named Engineering Programs and the General Criteria (professional component) by being involved with cooperative, part-time, and summer work experiences. In particular, the findings suggest that both undergraduate and graduate students believe that three areas have been greatly enhanced with engineering work. They include structural engineering, project management/scheduling and estimating, and teamwork. In addition, undergraduates perceive that their understanding of health and safety issues and ethical considerations has also increased. In contrast, graduate students believe that their knowledge of hydraulics, hydrology, and water resources, constructability, and economic factors has been enhanced by civil engineering work experiences.     

Optical Classification of Quartz Lascas by Artificial Neural Networks

A gradation method based on quartz lascas (lumps) transparency level is proposed. The samples were irradiated by transmitting light, and the images histograms were processed by artificial neural networks. Additionally, the results were compared to conventional classification methods, including density and visual analysis. The network designed with backpropagation architecture using 4 hidden layers of 10 neurons yielded to a relative error <24% in relation to manual classification, indicating a good agreement to the miners criteria. Furthermore, the implementation of competitive learning with 5 neurons resulted in correct discrimination of samples regarding their optical characteristics with a completely non-subjective approach.     

Water Distribution Network Design Using Heuristics-Based Algorithm

Water distribution network that includes supply reservoirs, overhead tanks, consumer demand nodes, interconnecting pipes, lifting pumps, and control valves is the main mode of water supply for majority of the communities especially in urban areas. Supply of required quantity of water and at right time is the primary objective of water distribution network analysis. The analysis of water distribution networks can be broadly classified into design and operation problems and both problems have been the focus of many researchers over the past three decades. In the water distribution network design problems, the target is attaining the cost effective configuration that satisfies the minimum hydraulic head requirement at the demand nodes. In this paper, a new algorithm for design of water distribution network namely “heuristics-based algorithm” which completely utilizes the implicit information associated with the water distribution network to be designed has been proposed and validated with two water distribution networks. It is found that the proposed algorithm performs well for the least-cost design of water distribution networks.     

Teaching Hydraulic Design Using Equation Solvers

Civil engineering graduates need to be competent in hydraulic theory, as well as in the application of that theory to the solution of practical problems. Teachers of hydraulic design are faced with the dilemma that most realistic hydraulics problems are too complex to solve by hand, while most commercially available software packages obscure the theoretical background for program algorithms. Equation solvers provide a valuable tool for bridging these gaps. Students can develop an appropriate linear or nonlinear mathematical model to depict a realistic system, then use an equation solver package to solve that model for any combination of input data desired. Computer-based studio classrooms further enhance the learning experience by allowing students to solve problems under the instructor's supervision during class periods. This paper will describe how effective equation solvers and the studio classroom can be in teaching hydraulic design for water distribution systems and open-channel flow. The theory is developed in class through the use of printed notes. Students then develop the nonlinear mathematical model for a simple example, solve the model using an equation solver, and check the correctness of the solution. Students are able to investigate the dynamic response and the sensitivity of the model by varying the equation solver input variable values. Next they apply the theory and solution methods to a practical applications exercise. The final step is to complete a comprehensive, realistic design problem. Students are required to present their results to the class at all stages of the process. Course-end evaluation scores have risen significantly since the class has been converted to the studio format. Student comments indicate that they think equation solvers are a valuable engineering design tool, not only for learning, but in professional practice as well. The instructor has observed that students learn and retain the theory much better when they can apply it immediately to realistic problems. Much more realistic and sophisticated quizzes can be given when the students have computers available to assist with the analysis.     

Performance-Based Optimization of Land and Water Resources within Irrigation Schemes. I: Method

Optimum land and water allocation to different crops grown in different regions of an irrigation scheme is a complex process, especially when these irrigation schemes are characterized by different soils and environment and by a large network of canals. At the same time if the water supply in the irrigation schemes is limited, there is a need to allocate water both efficiently and equitably. This paper describes the approach to include both productivity (efficiency) and equity in the allocation process and to develop the allocation plans for optimum productivity and/or maximum equity for such irrigation schemes. The approach presented in this paper considers the different dimensions of equity such as water distribution over the season, water distribution during each irrigation, and benefits generated. It also includes distribution and conveyance losses while allocating water equitably to different allocation units. This paper explains the approach with the help of the area and water allocation model which uses the simulation–optimization technique for optimum allocation of land and water resources to different crops grown in different allocation units of the irrigation scheme.     

Geotechnical Physical Modeling for Education: Learning Theory Approach

As physical modeling sees increasing use in geotechnical engineering education, there is a need for a strategic approach for integrating this powerful simulation technique into courses in a way that ensures the greatest benefit for students. For this reason, a learning theory approach, which recognizes the natural learning cycle of students, has been developed. The approach is based on a modified version of the learning theorist David Kolb’s “theory of experiential learning.” The approach emphasizes a variety of learning styles and thus is appealing to a broad range of students. The approach is relatively easy to apply to traditional geotechnical engineering coursework and requires only a modest effort to adopt. It is expected that by using this approach when designing course modules, instructors can increase the likelihood that comprehensive learning will take place. While this paper focuses on physical modeling for geotechnical engineering, the approach presented here has educational applications to an array of other civil engineering topics.     

Optimal Design of Level 1 Redundant Water Distribution Networks Considering Nodal Storage

A water distribution network (WDN) is designed to meet time-varying demands with sufficient pressure, taking into consideration an appropriate demand during peak hours. Therefore, a network has inherent redundancy in the sense that under abnormal conditions such as those arising due to pipe breaks or pump failures, deficiency in supply during peak hours can be met through additional supply during off-peak periods. However, this necessitates a storage facility at the consumer end of the network, which is normally available in the form of a sump or an overhead tank in developing countries. Such a storage enables the consumer to store water during the off-peak period and then use it during the peak period. Reliability of a WDN is assessed herein considering nodal storage, and an iterative method is proposed for the optimal design of Level 1 redundant WDNs, i.e., networks that can sustain a single pipe failure without affecting consumer services either in part or in full. The method is illustrated through an example and the designs of a network with and without storage are compared. Provision of a nodal storage is found to reduce the total cost of the network.     

Two-Point Linearization Method for the Analysis of Pipe Networks

This note proposes a new method for snapshot analysis of water distribution systems based on the commonly used gradient method. The proposed method uses a secant (intersecting the head-loss function in two points) instead of a tangent to approximate the pipe head-loss function. A theoretical model is developed for the flow range in which the secant approximates the head-loss function without exceeding a given allowable error. This scheme allows a tradeoff to be made between the allowable error and the number of iterations required to achieve convergence. The proposed method is applied to an example network to illustrate its application and benefits. It is argued that the number of iterations required to find a solution can be reduced significantly in both snapshot and extended-period simulations.     

Reliability, Risk, and Uncertainty Analysis Using Generic Expectation Functions

In engineering design and analysis, mathematical models that generally involve a number of uncertain parameters are frequently employed for decision making. Over the years, a number of techniques have been developed to quantify model output uncertainty contributed by uncertain input parameters. Typically, the methods that are easy to apply may give inaccurate estimates of model output uncertainty. Other methods that reliably produce very accurate results are either difficult to apply or require intensive computational effort. This paper describes the development of generic expectation functions as a function of means and coefficients of variation of input random variables. The generic expectation functions are straightforward to develop, and apply to problems related to reliability, risk, and uncertainty analysis. Several expectation functions based on commonly used probability distributions have been developed. Using them, any order of moment can be estimated exactly. It is found that if exact moments of the model output are available, one can find a good estimate of reliability, risk, and uncertainty of a system without knowing its model output distribution exactly. This technique is applicable when an output variable is a function of several independent random variables in multiplicative, additive, or combined (multiplicative and additive) forms. A practical example is presented to demonstrate the application of generic expectation functions.