Abstract Learning about complex systems is a fundamental engineering skill; it is often the first task of an engineer when faced with a complex problem in an unfamiliar context. The nature of learning is discussed. Contributions from cognitive psychology, artificial intelligence, management and sociology are drawn together to show that fundamentally, system learning is a matter of forming well-constructed system or conceptual models. The skills needed for this are discussed.

Abstract A generalized systems theory can be used in the identification and analysis of complex engineering problems. In a system formulation, an image or a model of an object which emphasizes certain important and critical properties is defined. System definition is usually the first step in an overall methodology formulated for achieving a set of objectives. This work represents the first step in a study that aims at the development of a real-time control system of construction activities. A hierarchical control system was determined to be the most suitable control scheme for construction activities. In this paper, a system definition of a construction activity is presented. The hierarchical system classification enables the decomposition of the overall construction activity into subsystems that represent the different underlying processes. Each process consists of several tasks. The classification method also allows the decomposition of the overall tasks into subtasks. This step is an important requirement for hierarchical control. The decomposition of tasks and activities allows accurate feedback decisions within the control scheme. The concepts developed in the paper were illustratively applied to define a concrete placement activity.

Abstract Real-time demand forecasts form the basis for the on-line computerised control of water distribution systems and affect the system security and reliability. This Paper investigates the methodologies for consumer demand modelling and prediction in a real-time environment. An approach based on time series analysis techniques is presented. A model using a combination of exponentially weighted mean and autoregressive structures is developed to predict the daily demand. A set of adaptive templates is used to allocate the daily demand to produce the diurnal demand profile. The models are established based on actual data from an actual distribution system. The performance of the practical implementation of the models is given and assessed. For the case study presented, it is found that the relatively simple time series models can be satisfactorily used to predict consumer demand for the real-time control purposes.

Abstract The study in this paper introduces the development of a knowledge-based expert system that helps to determine the suitability of FGD by-product in embankment construction. The expert system shell Personal Consultant Plus was used for this purpose. The knowledge base in the Embankment Construction Expert System (ECES) consists of a root frame and four subframes, and accommodates the production rules obtained from laboratory tests as well as an extensive literature review. An external program to calculate the embankment settlement was integrated into the system. A case study is presented to demonstrate the use of ECES. This study provides the embankment designer with a useful tool for making decisions on FGD by-product application as an embankment material.

Abstract Treatment of correlations between variables is necessary to establish realistic quantifications of uncertainty of decision variables such as project duration, cost and internal rate of return during the planning stages of an engineering project. This paper highlights some often ignored theoretical requirements necessary for a rigorous consideration of correlations and presents a general analytical procedure for eliciting and treating correlations between variables which is applicable to a broad class of engineering problems. The general case of a decision variable Y expressed in terms of an arbitrary function g(X) is examined, where X is a vector of random variables, some or all of which may be correlated. The theoretical requirement for a positive definite correlation matrix is described, along with a two stage process that leads to the elicitation of such a matrix. A variable transformation approach for treating correlations in moment analysis is used to derive the first four moments of Y. The ...

Abstract The observability problem in water systems consists in determining whether the available set of measurements provides sufficient information for the computation of the full system state (this latter operation is called state estimation): given a set of network measurements including some flow, head, consumption and/or resistance values, is it possible to derive all the other hydraulic unknowns of the network? This question is crucial especially for water systems, where the pipe resistances and the consumptions at the nodes are often roughly estimated. One may thus want to use all available measurements for calibrating or estimating these parameters. The aim of this contribution is to determine which variables are structurally observable from the available measurements in a systematic way. This is achieved using a graph-theoretic approach. We split the variables into three classes: the redundant variables, the variables which can be estimated without redundancy (they will be called calculable) and finally the unobservable variables. This provides some insight in where to add new measurements for improving the observability of the system. This classification is also used to formulate a well conditioned least square problem and to compute the values of redundant and calculable variables. We then show how to use optimization techniques for solving this least square problem, with a special emphasis put on the choice of the weighting coefficients, depending on what kind of result is expected (calibration, leak detection, …). Finally, we present results obtained for calibration and leak detection on a real life network, and we give an idea of how to extend these results obtained in the static case to the case when the dynamical behaviour of the system is taken into account (measurements being available at every time).

Abstract The water industry is heavily regulated, and is in a monopolistic situation, so it is restricted in the charges it can levy, while it has to meet the full demands of its customers. Therefore the economist's approach to optimization, based on the assumption that volume is the sole determinant of cost and revenue changes, cannot be used. Thus an alternative approach is required. The aim of strategic optimization at North West Water Ltd is to produce an operating procedure for the major sources, to meet a declared demand, while maintaining a specified minimum storage volume, at minimum cost.

Abstract This paper introduces a system designed to improve the accuracy of short term water demand forecasts by combining a proven mathematical prediction model with a knowledge base of information relating to non-cyclic abnormal demand occurrences. In part one of the paper, a prototype system is described that comprises a mathematical prediction module and a simplified knowledge base of FORTRAN rules. The results derived from the testing of the prototype show that the methodology is capable of providing significant improvements in prediction accuracy when the normal cyclic demand pattern is disrupted. Part two of the paper describes the steps taken towards implementing the elements that make up a full combined forecasting system in the light of the knowledge gained from building and testing the prototype.

Abstract The land allocation problem is one of selecting, from a set of available land units or parcels, a subset of units displaying certain desirable characteristics or having special morphology. Several discrete programming models are presented for the problem of allocating an area of land for development when the shape of the resulting land area is a primary concern. The solution procedure is shown to be satisfactory for small to medium sized problems and can accommodate simple (rectangular) shapes.

Abstract An optimality criteria (OC) method is presented for weight optimization of space frames having general cross-sectional relationships. The space frames support a sizeable amount of non-structural mass, while multiple natural frequency constraints, and minimum and maximum gauge restrictions are imposed on their design. The iterative design method involves alternately satisfying the constraints (scaling) and applying the Kuhn-Tucker (optimality) condition (resizing). The primary sizing variables (cross-sectional areas), and indirectly the secondary ones (two principal moments of inertia and a torsional constant), are uniformly scaled to the constraint surfaces using a nonlinear closed-form formulation. No exact scaling formulation for this class of problem has been proposed and tested in the optimization literature hitherto. The closed-form scaling procedure is united with an adaptable design strategy in which linear extrapolates of past-scaled design vectors are coupled with automatically-tuned OC recursive methods. Elementary design examples are presented to demonstrate the method. On average, the method achieves a stable upper-bound convergence of weight minima, as it quickly dissolves the (sometimes violent) oscillations of scaled weights in the iteration history. Most of all, the present design strategy eliminates the need for adjustments of internal parameters during the redesign phase.