An integrated design and manufacturing approach allows economic decisions to be made that reflect an entire system design as a whole. To achieve this objective, we have developed and utilized integrated cost and engineering models within a focused design perspective. A framework for the integrated design of an aircraft system with a combined performance and economic perspective is described in this article. This framework is based on the concept of Design Justification using a Design-for-Economics approach. We have developed a knowledge-based system that can be used to evaluate aircraft structural concept material and process selections. The framework consists of the knowledge-based system, integrated with numerical analysis tools including an aircraft performance/sizing code and a life-cycle cost analysis code. Production cost estimates are applied for evaluation of process trades at the subcomponent level of design. Life-cycle cost estimates are used for evaluation of process trades at the system level. Results of a case study are presented for several advanced wing structural concepts for a future supersonic commercial transport aircraft. Cost versus performance studies indicate that a high-speed civil transport aircraft with a hybrid wing structural concept, though more expensive to manufacture than some homogeneous concepts, can have lower direct operating costs due to a lower take-off gross weight and less mission fuel required.

This paper proposes a fuzzy neural network (FNN) based approach to construct an individual-oriented car-following system. The feature of this system is firstly to incorporate a personal risk-taking factor in addition to other mechanical factors as the input parameters. Through the learning capability of artificial network, the complex membership functions between the input factors and the output (i.e., the appropriate car-following headway) can be efficiently established, and then the fuzzy logic rules can be properly constructed. The performance of the FNN system is finally assessed against the field data. The results are inspiring that the system is proven capable of providing highly accurate predictions of the required car-following headways from person to person at various speeds. The success of this study provides some clues of utilizing FNN techniques in exploring some individual-oriented machines.

The paper describes and evaluates three methods of modelling earthquake accelerograms using artificial neural networks and linear predictors. These are a radial-Gaussian neural networking system, a linear predictor, and a hybrid of these two. Two methods of using these models to predict ground acceleration are adopted. The first is based on a direct prediction of ground acceleration at some point in time in the future from a series of ground accelerations that occurred earlier in the earthquake event. The second is a recursive approach whereby a model predicts a sequence of future ground accelerations by feeding back its predictions to its inputs. The performances of the models are tested using the 1985 Mexico earthquake and aftershock. The linear predictor and hybrid models perform best at direct prediction while none of the models perform particularly well at recursive prediction. The paper concludes with an outline of some areas for future work.

The paper proposes and evaluates a binary network architecture and complementary training algorithm designed for pattern classification, with applications in a variety of engineering problems. The advantages of the system are that it can always converge on zero error for a set of unambiguous training patterns (if required), converges rapidly, and circumvents the issue of how many hidden neurons to incorporate in a network. The main benefit of the proposed system over Hamming networks, its main counterpart, is that it can group patterns into different classes along any boundary. The system is shown to outperform 1) the Hamming network for a character recognition problem where the images are subject to both position change and noise; and 2) a radial-Gaussian network in a truck-type classification problem. A variant of the system, where hidden neuron thresholds are set to zero, is shown to further improve performance if a comprehensive set of noise-free input patterns are available for training.

A new methodology for solving resource leveling problems is introduced using Artificial Neural Networks (ANN). This paper describes a new efficient and robust approach which is unique to those utilized by traditional heuristic and optimization resource leveling techniques. The Resource Leveling Artificial Neural Network (RLANN) exploits advantages of both Hopfield networks and competition-based artificial neural networks. The universal scheme of the RLANN is applicable to construction project networks produced with Critical Path Method (CPM), in forms of either arrow or precedence diagrams. The scheme is comprised of two layers, an input and a competition layer, of artificial node matrices fully connected by links. Solving mechanisms inside the RLANN are based on an equation of motion and a competition strategy that control the level of daily resource usage. While the equation of motion governs activities to be shifted within schedule constraints, the competition process finds the best positions for the activities to achieve optimum results. The approach is simple and can be implemented on either a personal computer or a parallel processing device. The solutions produced are comparable to, or better than, those generated by other heuristic or optimization techniques. This paper describes the development of the RLANN, its solving mechanisms, and its uses in construction resource leveling problems. The comparison of the result of the RLANN to those of other traditional techniques is also included. The conclusions highlight the applicability of this model to other civil engineering problems.