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

Forecasting and Forecast Combination in Airline Revenue Management Applications

Predicting a variable for a future point in time helps planning for unknown future situations and is common practice in many areas such as economics, finance, manufacturing, weather and natural sciences. This paper investigates and compares approaches to forecasting and forecast combination that can be applied to service industry in general and to airline industry in particular. Furthermore, possibilities to include additionally available data like passenger-based information are discussed

Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction

British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of dimensionality of input space due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using seven datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four popular continual learning algorithms and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System

A Distributed Outstar Network for Spatial Pattern Learning

The distributed outstar, a generalization of the outstar neural network for spatial pattern learning, is introduced. In the outstar, signals from a source node cause weights to learn and recall arbitrary patterns across a target field of nodes. The distributed outstar replaces the outstar source node with a source field of arbitrarily many nodes, whose activity pattern may be arbitrarily distributed or compressed. Learning proceeds according to a principle of atrophy due to disuse, whereby a path weight decreases in joint proportion to the transmitted path signal and the degree of disuse of the target node. During learning, the total signal to a target node converges toward that node's activity level. Weight changes at a node are apportioned according to the distributed pattern of converging signals. Three synaptic transmission functions, by a product rule, a capacity rule, and a threshold rule, are examined for this system. The three rules are computationally equivalent when source field activity is maximally compressed, or winner-take-all. When source field activity is distributed, catastrophic forgetting may occur. Only the threshold rule solves this problem. Analysis of spatial pattern learning by distributed codes thereby leads to the conjecture that the unit of long-term memory in such a system is an adaptive threshold, rather than the multiplicative path weight widely used in neural models.British Petroleum (89-A-1204); Advanced Research Projects Agency (ONR N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100

Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA

In this study two approaches are applied for the prediction of the economic recession or expansion periods in USA. The first approach includes Logit and Probit models and the second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gaussian and Generalized Bell membership functions. The in-sample period 1950-2006 is examined and the forecasting performance of the two approaches is evaluated during the out-of sample period 2007-2010. The estimation results show that the ANFIS model outperforms the Logit and Probit model. This indicates that neuro-fuzzy model provides a better and more reliable signal on whether or not a financial crisis will take place.ANFIS, Discrete Choice Models, Error Back-propagation, Financial Crisis, Fuzzy Logic, US Economy

Linearity Testing Against a Fuzzy Rule-based Model

In this paper, we introduce a linearity test for fuzzy rule-based models in the framework of time series modeling. To do so, we explore a family of statistical models, the regime switching autoregressive models, and the relations that link them to the fuzzy rule-based models. From these relations, we derive a Lagrange Multiplier linearity test and some properties of the maximum likelihood estimator needed for it. Finally, an empirical study of the goodness of the test is presented.fuzzy rule-based models, time series, linearity test, statistical inference

Study on adaptive control of nonlinear dynamical systems based on quansi-ARX models

制度:新 ; 報告番号:甲3441号 ; 学位の種類:博士(工学) ; 授与年月日:15-Sep-11 ; 早大学位記番号:新576