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

Perception of mathematics game’s design for primary school: based on teachers’ opinions

Unmistakable methods can be used for learning, and they can be looked at in a few viewpoints, particularly those identified with learning results. In this paper, we introduce an examination with a specific end goal to think about the design adequacy and development’s requirement of a game based learning (GBL) approach that is about to be used in LINUS screening for mathematics subject in primary school. The approach includes multiple interaction forms regarding addition and subtraction operation in mathematics based on LINUS constructs. Ten teachers from three different school located in Batu Pahat have participated in the study. The investigations involving survey activity by using questionnaire as the instrument. While breaking down the results, the outcomes demonstrated that the kids observed the amusement to be all the more fulfilling if there are less levels and more colours. Since the survey were conducted to a very common type of school in Malaysia, we believe game that is about to be built based on opinion gained could be utilized as an effective instrument in primary schools to strengthen pupils' lessons

A type-2 fuzzy system model for reducing bullwhip effects in supply chains and its application in steel manufacturing

AbstractThe purpose of this paper is to evaluate and reduce the bullwhip effect in fuzzy environments by means of type-2 fuzzy methodology. In order to reduce the bullwhip effect in a supply chain, we propose a new method for demand forecasting. First, the demand data of a real steel industry in Canada is clustered with an interval type-2 fuzzy c-regression clustering algorithm. Then, a novel interval type-2 fuzzy hybrid expert system is developed for demand forecasting. This system uses Fuzzy Disjunctive Normal Forms (FDNF) and Fuzzy Conjunctive Normal Forms (FCNF) for the aggregation of antecedents. An interval type-2 fuzzy order policy is developed to determine orders in the supply chain. Then, the results of the proposed method are compared with the type-1 fuzzy expert system as well as the type-1 fuzzy time series method in the literature. The results show that the bullwhip effect is significantly reduced; also, the system has less error and high accuracy

Learning fuzzy systems: an ojective function-approach

One of the most important aspects of fuzzy systems is that they are easily understandable and interpretable. This property, however, does not come for free but poses some essential constraints on the parameters of a fuzzy system (like the linguistic terms), which are sometimes overlooked when learning fuzzy system automatically from data. In this paper, an objective function-based approach to learn fuzzy systems is developed, taking these constraints explicitly into account. Starting from fuzzy c-means clustering, several modifications of the basic algorithm are proposed, affecting the shape of the membership functions, the partition of individual variables and the coupling of input space partitioning and local function approximation

Color Image Segmentation Using Fuzzy C-Regression Model

Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c-means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C-Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley’s segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms

Algoritmos de Agrupamiento en la Identificación de Modelos Borrosos

[ES] La aplicación de las técnicas de agrupamiento borroso para la identificación de modelos borrosos se está extendiendo cada vez más. Sin embargo, y dado que su origen es bien distinto a la ingeniería de control, aparecen numerosos problemas en su aplicación en la identificación de modelos locales de sistemas no lineales para control. En este trabajo se revisan las principales técnicas de agrupamiento para la identificación de modelos borrosos, incluyendo propuestas propias que permiten desarrollar modelos que mejoran (respecto a algoritmos previamente existentes) la interpretabilidad y el descubrimiento de estructuras afines locales en los modelos borrosos obtenidos.Parcialmente financiado por el proyecto CICYT DPI2002-0525 (Ministerio Ciencia y Tecnología).Diez Ruano, JL.; Navarro Herrero, JL.; Sala Piqueras, A. (2010). Algoritmos de Agrupamiento en la Identificación de Modelos Borrosos. Revista Iberoamericana de Automática e Informática industrial. 1(2):32-41. http://hdl.handle.net/10251/146622OJS32411

New methods for discovering local behaviour in mixed databases

Clustering techniques are widely used. There are many applications where it is desired to find automatically groups or hidden information in the data set. Finding a model of the system based in the integration of several local models is placed among other applications. Local model could have many structures; however, a linear structure is the most common one, due to its simplicity. This work aims at finding improvements in several fields, but all them will be applied to this finding of a set of local models in a database. On the one hand, a way of codifying the categorical information into numerical values has been designed, in order to apply a numerical algorithm to the whole data set. On the other hand, a cost index has been developed, which will be optimized globally, to find the parameters of the local clusters that best define the output of the process. Each of the techniques has been applied to several experiments and results show the improvements over the actual techniques.Barceló Rico, F. (2009). New methods for discovering local behaviour in mixed databases. http://hdl.handle.net/10251/12739Archivo delegad

PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.Comment: This paper has been accepted for publication in Information Science Journal 201