Abstract. Bamboo is a rapid renewable plant that has a fast growth rate as compared to trees, which increases its suitability to be used as a sustainable source for wood industry, especially in construction works. Due to the lack of understanding on bamboo properties, the utilization of bamboo in construction has always been neglected. This paper presents an investigation on the mechanical properties of four species of treated bamboos that are available in Malaysia, which include Bambusa Vulgaris, Dendrocalamus Asper, Schizostachyum Grande, and Gigantochloa Scortechinii. A mechanical testing was carried out in various parts along the culm of these bamboo species in order to examine the differences of their compressive strength and tensile strength. The strength development and moisture content of these bamboo species were also monitored at a five-month interval. The results showed that Bambusa Vulgaris, Dendrocalamus Asper, and Gigantochloa Scortechinii possess excellent mechanical properties in compression and tensile strength, which indicate a good quality to be used as a construction material. As bamboo offers promising advantages, thus, it is suitable to be used as a substitute in place of structural timber in construction, which indirectly facilitates the preservation of the global environment.

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The most commonly accepted method in evaluation of the mechanical properties of metals would be the tension test. Its main objective would be to determine the properties relevant to the elastic design of machines and structures. Investigation of the engineering and true Stress-strain relationships of three specimens in conformance with ASTM E 8-04 is the aim of this paper. For the purpose of achieving this aim, evaluation of values such as ultimate tensile strength, yield strength, percentage of elongation and area reduction, fracture strain and Young's Modulus was done once the specimens were subjected to uniaxial tensile loading. The results indicate that the properties of steel materials are independent from their thickness and they generally yield and fail at the same stress and strain values. Also, it is concluded that the maximum true stress values are almost 15% higher than that of the maximum engineering stress values while the maximum true strain failure values are 1.5% smaller than the maximum engineering strain failure values.

An improved genetic algorithm procedure is introduced in this work based on the theory of the most highly fit parents (both male and female) are most likely to produce healthiest offspring. It avoids the destruction of near optimal information and promotes further search around the potential region by encouraging the exchange of highly important information among the fittest solution. A novel crossover technique called Segmented Multi-chromosome Crossover is also introduced. It maintains the information contained in gene segments and allows offspring to inherit information from multiple parent chromosomes. The improved GA is applied for the automatic and simultaneous parameter optimization and feature selection of multi-layer perceptron network in medical disease diagnosis. Compared to the previous works, the average accuracy of the proposed algorithm is the best among all algorithms for diabetes and heart dataset, and the second best for cancer dataset.

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<p class="Abstract">This study presents characterization of cracking in pavement distress using image processing techniques and <em>k</em>-nearest neighbour (kNN) classifier. The proposed semi-automated detection system for characterization on pavement distress anticipated to minimize the human supervision from traditional surveys and reduces cost of maintenance of pavement distress. The system consists of 4 stages which are image acquisition, image processing, feature extraction and classification. Firstly, a tool for image acquisition, consisting of digital camera, camera holder and tripod, is developed to capture images of pavement distress. Secondly, image processing techniques such as image thresholding, median filter, image erosion and image filling are applied. Thirdly, two features that represent the length of pavement cracking in <em>x</em> and <em>y</em> coordinate system namely <em>delta_x</em> and <em>delta_y</em> are computed. Finally, the computed features is fed to a kNN classifier to build its committee and further used to classify the pavement cracking into two types; transverse and longitudinal cracking. The performance of kNN classifier in classifying the type of pavement cracking is also compared with a modified version of kNN called fuzzy kNN classifier. Based on the results from images analysis, the semi-automated image processing system is able to consistently characterize the crack pattern with accuracy up to 90%. The comparison of analysed data with field data shows good agreement in the pavement distress characterization. Thus the encouraging results of semi-automated image analysis system will be useful for developing a more efficient road maintenance process.</p>