Building information modeling (BIM) and green building index (GBI) assessment framework for non-residential new construction building (NRNC)

The global construction industry endorsed Building Information Modeling (BIM) and its many advantages. However, despite this endorsement, BIM still failed to attract Malaysian companies to use BIM in green building assessment, especially for the assessment of Green Building Index (GBI), and maintain GBI certification during building occupancy using BIM features. The main issue of utilizing BIM as a GBI assessment tool is the applicability of BIM Tools to digitalize GBI credit by design team, which results in the digitization of GBI criteria into BIM Model. This study aims to identify common components related to the capability of BIM to digitalize and assess GBI criteria. These components include BIM uses and tools and GBI criteria and processes. This study applied quantitative and qualitative approaches to collect data. The quantitative approach used questionnaires, which were distributed to 900 GBI members, i.e. GBI certifiers and facilitators. The survey generated a response rate of 32% during eight months of data collection. The results were analyzed using SPSS and SmartPLS. Four model categories were identified, namely, BIM uses, BIM tools, GBI criteria and GBI certification process. These categories were used to assess the BIM–GBI framework. The results obtained from the questionnaire showed that only 16 BIM uses must be included in the BIM execution plan of the GBI project for assessment purposes. The results also showed that the BIM tools present different levels of effect on the GBI criteria. The capability of BIM to assess GBI could be stronger in the design assessment (DA) than in the operation assessment, which supports the suggested BIM–GBI assessment framework. The second data collection was conducted through a focus group interview with BIM and GBI experts. Two interview sessions were conducted. Results show that the assessment method has a significant correlation in the BIM– GBI framework. The following categories were identified for the BIM assessment framework: BIM uses, BIM tools, and control, which were based on the GBI criteria for scoring and certification. Findings from the BIM and GBI assessment method framework show that GBI credits can be digitalized using different BIM uses directly and indirectly assessed by BIM tools for each GBI credit in both GBI assessment process. Based on the qualitative result of this research showed that BIM can help the design team to achieve 55% point in design assessment (DA) only and this helps the building to achieve GBI certification in level 4 of certified rating. On the other hand, 45% points of GBI credits can be digitals in completion and verification assessment (CVA). The framework provides a guide for the design team and facility management in digitalizing and assessing GBI criteria using BIM application during design assessment (DA) and completion and verification assessment (CVA) for new nonresidential constructions. The framework also offers and provides insights that will enable designers to understand the relationship between BIM and GBI criteria, which will contribute to BIM integration in Stage 3 and automate GBI assessment for the Malaysian construction industry

Quadrature Phase Shift Keying (QPSK) Modulator Design using Multi-Port Network in Multilayer Microstrip-Slot Technology for Wireless Communication Applications

The design of the quadrature phase shift keying (QPSK) modulator by using a multi-port network is proposed in this article for the use in wireless communication applications. The multi-port network is in the form of multilayer microstrip-slot technology. This multi-port network is composed of three 3-dB rectangular-shaped directional couplers with virtual stubs and an equal power division divider with in-phase characteristic. The design is performed by applying a full-wave electromagnetic simulation software, CST Microwave Studio (CST MWS). Keysight’s Advanced Design System (ADS) is applied in analyzing and evaluating the QPSK constellation of the proposed modulator. This comparatively small size of proposed design has been fabricated, and its wideband performance of 2 to 6 GHz is verified

Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949