Cross-laminated timber (CLT) is one of the preferred engineered timber products (ETP) used in the construction and building industry. CLT is an orthogonal and laminar structure that can be used as full-size load-bearing structural elements such as wall, floor element as well as a linear timber member. The timber pieces are visually graded before being manufactured into CLT panels. As a result, CLT is able to provide enhanced stability and performance compared with its solid timber counterparts. In order to use CLT products in the construction of timber structures, characteristic bending and shear properties are important values in design. Thus, this chapter provides an overview of the bending and shear properties of CLT made from different timber species with various densities including timbers such as softwood, temperate hardwood as well as tropical hardwood. The factors influencing their properties are summarized and discussed. Their respective failure modes are also reported and discussed. This chapter also covers the general manufacturing process and the applications of CLT in the construction and building sector. Limitations, challenges dealing with the applications of CLT in the construction industries and the future of CLT are also discussed.

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Clustering analysis is the process of identifying similar patterns in various types of data. Heterogeneous categorical data consists of data on ordinal, nominal, binary, and Likert scales. The clustering solution for heterogeneous data clustering remains difficult due to partitioning complex and dissimilarity features. It is necessary to find a solution to highquality clustering techniques to efficiently determine the significant features of the data. This paper emphasizes using the firefly algorithm to reduce the distance gap between features and improve clustering performance. To obtain an optimal global solution for clustering, we proposed a hybrid of mini-batch kmeans (MBK) clustering-based entropy distance measures (EM) with a firefly optimization algorithm (FA). This study compares the performance of hybrid K-Means, Agglomerative, DBSCAN, and Affinity clustering models with EM and FA. The evaluation uses a variety of data from the timber perception survey dataset. In terms of performance, the proposed MBK+EM+FA has superior and most effective clustering. It achieves a higher accuracy of 96.3 percent, a 97 percent F-measure, a 98 percent precision, and a 97 percent recall. Other external assessments revealed that the Homogeneity (HOMO) is 79.14 percent, the Fowlkes-Mallows Index (FMI) is 93.07 percent, the Completeness (COMP) is 78.04 percent, and the V-Measure (VM) is 78.58 percent. Both proposed MBK+EM+FA and MBK+EM took about 0.45s and 0.35s to compute, respectively. The excellent quality of the clustering results does not justify such time constraints. Surprisingly, the proposed model reduced the distance measure of all heterogeneous features. The future model could put heterogeneous categorical data from a different domain to the test.