11 research outputs found
تبیین اثر مؤلفههای فرهنگ ملی در پذیرش فناوری مدلسازی اطلاعات ساختمان (BIM) در شرکتهای ساختمانی استان تهران
It appears that Building Information Modeling with its reported benefits can help solve the inefficiency of Iran's construction industry. However, a review of some previous literature shows that the adoption status is unfavorable. There are various obstacles including legal, financial, technical, and behavioral ones to adopting BIM. One of the behavioral barriers to BIM adoption is cultural factors. Factors that make users resist change and fear new technology include the fear that they will not be able to work with new technology or that their organizational position will be endangered. The dimensions of national culture in each country or organization vary and as a result, the adoption of new technologies that are affected by these dimensions can be different. As a result, the conceptual model of the present study was developed with the aim of explaining the effect of national culture dimensions on BIM adoption in the first-ranked construction companies in Tehran province. In this conceptual model, the effect of five dimensions of national culture on the three variables of BIM adoption (TAM model) was evaluated. In this way, a researcher-made questionnaire was prepared and taken from the variables of the conceptual model and was distributed in four categories: employer, consulting, Design and Build, and contracting company. Analysis of 95 valid samples was performed using SPSS22 and SmartPLS3 software and it was shown that the adoption rate of BIM in construction companies ranked one in Tehran province, being lower than average. Also, the lower the power difference and uncertainty avoidance, individualism, and masculinity, the more members of companies find BIM implementation beneficial. Also, as risk-taking increases, company members find it easier to use BIM and are more eager for its adoption. The findings of the present study help construction companies that intend to adopt BIM technology for the first time to move towards adoption and implementation of BIM with stronger encouragement and support
INTEGRATION OF BIM AND REAL-TIME SENSOR DATA TO ENHANCE FACILITY MANAGEMENT
In the construction industry, much of the focus is on the design and construction phase, while the most extended and costly phase of building life cycle is related to the operation of the building. Availability, accessibility, reliability, and updating of building information and the appropriate tools to manage this information are critical in facility management. During operation, real-time data of the building (e.g., temperature, humidity) that reflects the actual condition of the building can be measured using sensors. Building Information Modeling (BIM) has the capability of integrating different technologies, thus providing a suitable platform for managing such critical information. Sensor data act as a data repository for the BIM model. The integration of BIM and real-time sensor data provides a powerful platform to visualize, monitor, and process building performance levels in a timely and automated manner. Although integrating sensor data and the BIM model was
explored in the previous studies, processing data to be added into the BIM
model was performed with a delay. In this study, visualization, monitoring, and
processing data are performed in a timely and automated manner by employing an Application Programming Interface (API). Moreover, the developed system
provides maintenance information promptly and reduces extra repair context of
the BIM. This procedure enhances the efficiency of facility management,
emergency management, and maintenance for buildings. The developed framework also reduces the need for monitoring maintenance data manually, resulting in lowering the cost of operating the building and increasing the level of
performance of the building simultaneously. The framework was validated in a
residential building. Sensor data were added to a database in an automatic and
timely manner via a user interface for the sake of visualization and data
monitoring. A customized API code was also utilized to process data and
evaluate the environmental conditions
PRESENTING KOHONEN NEURAL NETWORK MODEL FOR DETERMINING THE CONTRIBUTION OF EACH FACTOR INVOLVED IN THE DELAYS (CASE STUDY: ....)
With proper investigation of project delays, they can be made as lesson learning. So, in this research, a method has been presented to offer network- based pattern for identifying the reasons of delays. For this purpose, reasons of delay in Tehran-Shomal highway are indicated in a table for each group involved in the mentioned project and the main source of delays of the four factors involved in the project: employer, contractor (delay of materials and equipment and human resource, supplier delays, delay in transportation and warehouse that is the prerogative of contractor), consultant, and external factors were identified and categorized. Then, according to the experts' opinions and interviews conducted with agents working in projects, all items of
delays have been scored from 0 to 10 since 1994 to 2015 in two cases:, the possibility of delays in the project of Tehran-Shomal and the intensity rate and the impact of delays. In the last section, for the first time, delays in Kohonen self-organizing neural network have been evaluated using MATLAB in the program of SOFM. Delays in research findings that are not distinguished in two ways are not distinct, and overlapp each other