BIMp-Chart—A Global Decision Support System for Measuring BIM Implementation Level in Construction Organizations

Malik, Qurratulain; Nasir, Abdur Rehman; Muhammad, Rabiah; Thaheem, Muhammad Jamaluddin; Ullah, Fahim; Khan, Khurram Iqbal Ahmad; Hassan, Muhammad Usman

doi:10.3390/su13169270

Cited by 20 publications

(23 citation statements)

References 76 publications

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“…The main idea of this study highlights that the relationship between digital technology and BIM is interrelated [24], [25], [26]. Students from tertiary education institutions need to master both theories and digital skills during their undergraduate study, where BIM is emphasized as part of information technology [3], [7], [27].…”

Section: Introductionmentioning

confidence: 99%

[RETRACTED] Digital Technology Fluency and BIM Learning Environment in Undergraduate Construction Management

Wang¹,

Ismail²,

Yen³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Building Information Modelling (BIM) education is gaining more attention from various parties such as government, industry, and academicians. Many universities have integrated BIM into their curricula by using various approaches and teaching methods, but there is no commonly accepted approach to teaching BIM in Architecture, Engineering, and Construction (AEC) programs. This research aims to identify the level of BIM literacy among students in higher education institutions and its correlation with the components of BIM learning and the outcomes of BIM learning progress. A quantitative method was adopted where Partial Least Square -Structural Equation Modeling (PLS-SEM) analysis was used to analyze the data. Questionnaires were distributed to the respondents for data collection. A total of 33 respondents were chosen, consisting of second-year undergraduate Construction Management students at Universiti Sains Malaysia. The results were analyzed by using SPSS and SmartPLS 3. SPSS was used to study the correlation between variables, whereas SmartPLS was used to conduct other tests such as the path coefficient, bootstrapping, coefficient of determination (R-squared), effect size (F-squared), collinearity statistics (VIF), inner and outer VIF value, outer loading and outer weights. From the result findings, it was found that the respondents have less knowledge of the BIM software. The respondents also felt neutral toward improving their CGPA through BIM courses. Results showed that 3D parametric modeling and outcomes of BIM learning are correlated. For future research, the focus can be shifted to other BIM competencies, such as the managerial, functional, technical, and support aspects of BIM.

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Section: Introductionmentioning

confidence: 99%

[RETRACTED] Digital Technology Fluency and BIM Learning Environment in Undergraduate Construction Management

Wang¹,

Ismail²,

Yen³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…In recent years, increasing research efforts have been devoted to prompt the adoption of BIM such as identifying critical success factors (CSFs) for BIM implementation, developing a framework for implementing BIM, focusing on the benefits, barriers and risks of BIM implementation as well as assessing the level of BIM implementation (Porwal and Hewage, 2013;Won et al, 2013;Miettinen and Paavola, 2014;Bradley et al, 2016;Jung and Lee, 2016;Antwi-Afari et al, 2018;Khoshfetrat et al, 2020;Malik et al, 2021;Othman et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…, 2016; Jung and Lee, 2016; Antwi-Afari et al. , 2018; Khoshfetrat et al , 2020; Malik et al , 2021; Othman et al. , 2021).…”

Section: Introductionmentioning

confidence: 99%

A system dynamic model for assessing the level of BIM implementation in construction phase: a China case study

Zuo

Chang

et al. 2021

ECAM

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PurposeBuilding information modeling (BIM) is recognized as one of the technologies to upgrade the informatization level of the architecture engineering and construction (AEC) industry. However, the level of BIM implementation in the construction phase lags behind other phases of the project. Assessing the level of BIM implementation in the construction phase from a system dynamics (SD) perspective can comprehensively understand the interrelationship of factors in the BIM implementation system, thereby developing effective strategies to enhance BIM implementation during the construction phase. This study aims to develop a model to investigate the level of BIM implementation in the construction phase.Design/methodology/approachAn SD model which covered technical subsystem, organizational subsystem, economic subsystem and environmental subsystem was developed based on questionnaire survey data and literature review. Data from China were used for model validation and simulation.FindingsThe simulation results highlight that, in China, from 2021 to 2035, the ratio of BIM implementation in the construction phase will rise from 48.8% to 83.8%, BIM model quality will be improved from 27.6% to 77.2%. The values for variables “BIM platform”, “organizational structure of BIM” and “workflow of BIM” at 2035 will reach 65.6%, 72.9% and 72.8%, respectively. And the total benefits will reach 336.5 billion yuan in 2035. Furthermore, the findings reveal five factors to effectively promote the level of BIM implementation in the construction phase, including: policy support, number of BIM standards, owners demand for BIM, investment in BIM and strategic support for BIM.Originality/valueThis study provides beneficial insights to effectively enhance the implementation level of BIM in the construction phase. Meanwhile, the model developed in this study can be used to dynamically and quantitatively assess the changes in the level of BIM implementation caused by a measure.

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“…As a result, the authors suggested two scheduling methods for sensor deployment: heuristic and ant colony. The solution to the cover set issue, as stated in the preceding article, is explained in [49][50][51][52]. The authors described the learning automata algorithm in this paper.…”

Section: Related Workmentioning

confidence: 99%

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Akram

Munawar

Kouzani

et al. 2022

Sensors

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Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

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BIMp-Chart—A Global Decision Support System for Measuring BIM Implementation Level in Construction Organizations

Cited by 20 publications

References 76 publications

[RETRACTED] Digital Technology Fluency and BIM Learning Environment in Undergraduate Construction Management

[RETRACTED] Digital Technology Fluency and BIM Learning Environment in Undergraduate Construction Management

A system dynamic model for assessing the level of BIM implementation in construction phase: a China case study

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

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