Presents basic theory of prestressed concrete along with the load balancing, working-load and ultimate-load methods for prestressed concrete design. Material revised in light of substantial advances in the field includes: materials, prestressing systems, loss of prestress, shear and bond, camber and deflection. Design examples based on the 1977 ACI Code with its latest revisions. Appendix contains selected problems.

Data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. Currently, application oriented engineers are only concerned with their immediate problems, which results in an ad hoc method of problem solving. Researchers, on the other hand, lack an understanding of the practical issues of data-mining for real-world problems and often concentrate on issues that are of no significance to the practitioners. In this volume, we hope to remedy problems by (1) presenting a theoretical foundation of data-mining, and (2) providing important new directions for data-mining research. A set of well respected data mining theoreticians were invited to present their views on the fundamental science of data mining. We have also called on researchers with practical data mining experiences to present new important data-mining topics.

Presents basic theory of prestressed concrete along with the load balancing, working-load and ultimate-load methods for prestressed concrete design. Material revised in light of substantial advances in the field includes: materials, prestressing systems, loss of prestress, shear and bond, camber and deflection. Design examples based on the 1977 ACI Code with its latest revisions. Appendix contains selected problems.

Data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. Currently, application oriented engineers are only concerned with their immediate problems, which results in an ad hoc method of problem solving. Researchers, on the other hand, lack an understanding of the practical issues of data-mining for real-world problems and often concentrate on issues that are of no significance to the practitioners. In this volume, we hope to remedy problems by (1) presenting a theoretical foundation of data-mining, and (2) providing important new directions for data-mining research. A set of well respected data mining theoreticians were invited to present their views on the fundamental science of data mining. We have also called on researchers with practical data mining experiences to present new important data-mining topics.