The growing volume of information databases presents opportunities for advanced data analysis techniques from machine learning (ML) research. Practical applications of ML are very different from theoretical or empirical studies, involving organizational and human aspects, and various other constraints. Despite the importance of applied ML, little has been discussed in the general ML literature on this topic. In order to remedy this situation, we studied practical applications of ML and developed a proposal for a seven-steps process that can guide practical applications of ML in engineering. The process is illustrated by relevant applications of ML in civil engineering. This illustration shows that the potential of ML has only begun to be explored, but also cautions that in order to be successful, the application process must carefully address the issues related to the seven-step process. 1 Introduction Over the last several decades we have witnessed an explosion in information generat...

: The application of machine learning (ML) to solve practical problems is complex. Only recently, due to the increased promise of ML in solving real problems and the experienced difficulty of their use, has this issue started to attract attention. This difficulty arises from the complexity of learning problems and the large variety of available techniques. In order to understand this complexity and begin to overcome it, it is important to construct a characterization of learning situations. Building on previous work that dealt with the practical use of ML, a set of dimensions is developed, contrasted with another recent proposal, and illustrated with a project on the development of a decision-support system for marine propeller design. The general research opportunities that emerge from the development of the dimensions are discussed. Leading toward working systems, a simple model is presented for setting priorities in research and in selecting learning tasks within large projects. Cen...

This paper describes the development of computational support tools for practically successful engineering techniques. The paper reviews the requirements for manual Quality Function Deployment techniques, presents them, and discusses their limitations. It argues that computational support tools can alleviate most of these limitations and that a graph-based information representation for such techniques is an excellent choice for supporting both QFD techniques and their integration with other external CAD-related computational services. The paper presents an architecture for a computational QFD (CQFD) tool based on the graph-based modeling environment n-dim. It shows how this architecture supports most of the requirements for QFD techniques, in addition to providing many additional functionalities, and briefly illustrates how the CQFD tool will be used. Keywords Quality Function Deployment, TQM, design practice, 7 management tools, graph representation, n-dim, collaborative design, d...

Manual Quality Function Deployment (QFD) tools are limited in their use and their reuse. Computational tools can alleviate these limitations. In addition, Artificial Intelligence (AI) tools can further enhance the functionality of QFD tools. A graph-based information representation is proposed as the basis for integrating various QFD and AI tools. An architecture of a computational QFD (CQFD) tool based on the graph-based modeling environment n-dim is briefly discussed. The ideas are illustrated through the design of a cork remover. 1 INTRODUCTION The development of any product involves projecting its potential success in achieving its functional and commercial goals. Better quality designs that match customer needs and preferences and integrate manufacturing and other life-cycle issues early on into the design process are more likely to be competitive. Thus, there is significant concern in industry about quality product design that is addressed, among others, by Quality Function Dep...

Bridge databases contain significant information that can assist in future bridge management decisions. The effective utilization of this information requires facilities for the flexible extraction of usable knowledge in the form practitioners can use. We propose machine learning technology as a means to perform this knowledge extraction and to support the maintenance of bridge database information. An integrated system is proposed for supporting practitioners in the practical use of this technology. INTRODUCTION The premise of this research is that bridge databases, containing historical data on bridge performance and maintenance decisions, contain knowledge that can be used to enhance future decision making. The major impediment on using this vast amount of data in practice is the problem of making the knowledge embedded in the data explicit for decision makers. While the development of new database technologies, comprising the majority of present efforts in bridge management resea...

Most research on the application of machine learning to engineering problems have solved artificial problems. While research claimed to have reached results that would improve practice, these results have never been put to work by engineers themselves in solving their problems. A different approach of doing research on machine learning application is presented and a system design that may result in tangible practical results is outlined. The development of this system is underway. INTRODUCTION In order to make machine learning (ML) techniques usable for engineers, a methodological shift is required in the way ML research is perceived, planned, and executed. Past investigations that dealt with the development of ML techniques for solving engineering problems mostly developed ideas that were tested on simplified artificial problems. Thus, researchers could not demonstrate that their ideas had practical implications. With no direct connection between research and practice, researchers w...

Symbolic machine learning techniques can extract flexible and comprehensible knowledge from empirical data of material behavior. The diversity of symbolic machine learning techniques offers potential to match the requirements of many tasks when models of material behavior need to be created from data. We develop a series of steps for generating material behavior from empirical data and exemplify some of them on several small datasets. We discuss some of the issues that govern knowledge extraction and as a by-product, demonstrate that symbolic learning techniques are functionally superior to sub-symbolic learning for the task of comprehensible knowledge extraction. 1 Introduction Much of our knowledge about materials is expressed in phenomenological models created by interpreting empirical data. Such models that correlate between, for example, metal properties and their corrosion rate, can be used for various purposes such as predicting the loss of material over time or guiding the fut...

This paper reports on the use of machine learning systems for modeling existing engineering decision procedures. In this activity, various models of an existing decision procedure are constructed by using different machine learning systems as well as by changing their operational parameters and input. Individual models serve to focus on different aspects of the decision procedure and their combined use thus improves the understanding of the decision procedure which, in turn, can assist in its evaluation and subsequent debugging and improvement. This important modeling role of machine learning systems is exemplified by modeling an existing decision procedure that is used by engineers in selecting among available techniques for modeling groundwater flow and contaminant transport in a process of environmental decision making. This decision procedure was corrected and improved in the course of this work. The example demonstrates the practical utility of the modeling role of machine learnin...