Sri Lanka now has a prime market for "vertical growth" and the private sector has become the main developer. However, the government has also contributed to these developments through the condominiums constructed for low income families. These were built for the purpose of re-settling people from shanties and recently, Tsunami victims. These condominiums have shown significant failures and problems. This paper looks at some of these problems; specially maintenance. Three types of condominiums were selected to study. Interviews were conducted with management corporations, selected occupants and professionals in the relevant authorities. The study focused on understanding maintainability problems related to technical facilities, management, social condition, and legal background. Twenty five technical management, social and legal related problems were observed that occur during the maintenance and operational stages. Some of them have become a huge burden to the government. Further, the study identified eight strategies from substantive experts to eliminate these issues now and in future attempts.

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According to psychologists there are six types of universal facial expressions namely, "Fear", "Surprise", "Anger", "Sad", ''Disgust'' and "Happy". Holistic recognition of these facial expressions from static images requires nonlinear classifiers capable of operating on noisy highdimensional feature spaces. Often Radial Basis Function networks (RBFN) are used for classification in these applications. Conventional RBF networks however, in spite of their capabilities in working with high-dimensional feature spaces, often fail to deliver satisfactory performance in these scenarios due to small training sample sets, noisy features anUor features not following the required class smcture. This paper presents an improved RBFN architecture that overcomes these problems through asymmetrical scaling of feature axes according to specific requirements of the class structure of the classification problemThe scaling factors are computed automatically from the available mining samples, without any explicit analysis of their multi-variate statistical properties. The proposed network yielded an overall recognition rate of over 92% for the 6 expression classes, and a smaller network sue compared to other types of RBFN classifiers.

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