Technological developments have made the construction industry efficient. The aim of this research is to solve communication interaction problems to build a project management platform using the interactive concept of natural language processing technology. A comprehensive literature review and expert interviews associated with techniques dealing with natural languages suggests the proposed system containing the Progressive Scale Expansion Network (PSENet), Convolutional Recurrent Neural Network (CRNN), and Bi-directional Recurrent Neutral Networks Convolutional Recurrent Neural Network (BRNN-CNN) toolboxes to extract the key words for construction projects contracts. The results show that a fully automatic platform facilitating contract management is achieved. For academic domains, the Contract Keyword Detection (CKD) mechanism integrating PSENet, CRNN, and BRNN-CNN approaches to cope with real-time massive document flows is novel in the construction industry. For practice, the proposed approach brings significant reduction for manpower and human error, an alternative for settling down misunderstanding or disputes due to real-time and precise communication, and a solution for efficient documentary management. It connects all contract stakeholders proficiently.

Data-driven housing-market segmentation has been given increasing prominence for its 4 objectiveness in identifying submarkets based on the housing data's underlying structures. 5However, although popular in existing literature, current statistical-clustering methods, when 6 handling high-dimensionality housing dataset, has been found to tend to loss low-variance 7 information of the dataset and be deficient in deriving the globally optimal number of 8 submarkets. Accordingly, with the intention of achieving more rigorous housing-market 9 segmentation in the case of high-dimensionality housing dataset, a swarm-inspired projection (SIP) algorithm is introduced by this study. A case study is then conducted using housing dataset of Taipei city to evaluate the predictive accuracy of submarkets' housing prices obtained using hedonic price models, and which are based on the segmentations resulted from both the proposed SIP and a statistical-clustering method using the combination of principal component analysis (PCA) and K-means clustering. The results show that, as compared to the use of PCA and K-means, our proposed SIP algorithm can obtain more optimal number of submarkets for segmentation, and the resulted submarkets are more homogenous and distinctive. This finding highlights the advantages of our proposed SIP algorithm in housing segmentation, and thus it can better help inform the further studies of market segmentationrelated problems.

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The objective of the research is aimed for a solution that is to establish the dynamic impact function of surrounding multi-attribute for house pricing. It is also able to measure the ripple effect and allows the hedonic parameter estimates to vary from point-to-point. A comprehensive literature review is carried out to obtain an adequate theoretical basis for the corresponding hypothesis and concepts. The proposed dynamic impact function for multi- attributes is then constructed based on the characteristics of surrounding facilities. Adopting the convenience sampling criteria of 95% confidence level on the data sampling and 10% limit of error in a 5−95% proportion, we collect the empirical data of 39 yearly house sales in the investigated urban areas of Taipei city focusing on housing prices and then utilize them for evaluating and adjusting the function. The actual house price and that of proposed function affected by Mass Rapid Transit (MRT) stations are analysed, resulting in the correlation coefficient at 0.946 (single attribute) and 0.944 (multi-attribute), respectively. The findings support that proposed function can highly represent the house pricing pattern and be an accurate tool for appraisers.

Salah satu jenis urban sprawl adalah leapfrog. Perambatan leapfrog merupakan jenis pengembangan yang melompat-lompat, tidak berpola dan tidak memiliki keterkaitan dengan lahan yang sudah terbangun sebelumnya, dan apabila dibiarkan, akan muncul konsekuensi-konsekuensi seperti menambahnya waktu perjalanan dan pencemaran lingkungan. Di Kota Malang, terdapat wilayah-wilayah dengan arahan kawasan pertanian yang memiliki indikasi terjadinya perkembangan leapfrog. Penelitian ini bertujuan untuk mengidentifikasi kawasan-kawasan yang mengalami pola perkembangan leapfrog di kawasan peri urban Kota Malang. Analisis yang digunakan dalam menentukan faktor-faktor yang berpengaruh dalam pembentukan pola perumahan leapfrog adalah confirmatory factor analysis, analytical hierarchy process, weighted overlay, dan buffer analysis. Diketahui bahwa terdapat tiga kriteria yang dapat digunakan untuk menjadi indikator terjadinya leapfrog yakni aksesibilitas, kepadatan penduduk, dan campuran penggunaan lahan (mix-used land) dan perumahan leapfrog di lokasi studi dibagi menjadi perumahan swadaya, yakni perumahan di Jalan Atletik, Jalan Bulu Tangkis, dan Jalan Ikan Tombro Barat, serta perumahan komersial yakni Green View Regency. Diketahui bahwa ada empat indikator yang berpengaruh, yakni ketersediaan infrastruktur pendukung, aksesibilitas, fasilitas umum, serta daya beli masyarakat. Terdapat perbedaan antara jenis rumah swadaya dan jenis rumah komersial, yakni tidak dipertimbangkannya ketersediaan kendaraan umum, biaya transportasi sehari-hari, serta kedekatan dengan fasilitas sekolah dasar bagi masyarakat yang tinggal di tipologi swadaya. Hasil penelitian ini diharapkan bisa menjadi acuan dalam pembuatan peraturan pengendalian perkembangan leapfrog menurut faktorfaktor yang berpengaruh.