Construction Planning and Scheduling of a Renovation Project Using BIM-Based Multi-Objective Genetic Algorithm

Nusen, Pornpote; Boonyung, Wanarut; Nusen, Sunita; Panuwatwanich, Kriengsak; Champrasert, Paskorn; Kaewmoracharoen, Manop

doi:10.3390/app11114716

Cited by 24 publications

(11 citation statements)

References 48 publications

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“…In addition, each record has a token representing the type of network connection, identifying whether the connection is a normal or malicious attack. In terms of attack types, the KDD99 dataset consists of four major categories of attacks covering a total of 39 specific intrusion types 11 . Twentytwo of these intrusion types appear in the training and test sets, while the remaining 17 appear only in the test set.…”

Section: Modelling and Solvingmentioning

confidence: 99%

Studying computer communication networks to identify errors using enhanced neural network algorithms

zhang

2024

Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023)

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The stability and reliability of computer communication networks are crucial for the proper functioning of modern society. However, network faults are inevitable, so efficient fault detection systems are needed to quickly identify and solve problems. This study aims to improve the performance of network fault detection by introducing improved neural network algorithms and applying deep learning techniques to this field. First, we studied in detail the various types of faults that may occur in computer communication networks and constructed the corresponding fault datasets. Then, we introduced improved neural network structures, including convolutional neural networks and recurrent neural networks, to better capture the spatio-temporal characteristics of network data. Experimental results show that the improved neural network algorithm exhibits excellent performance in network fault detection. Compared with traditional methods, our model achieves significant improvements in detection rate and accuracy. In addition, the model demonstrates adaptability to different fault types, including traditional faults and emerging network threats. In summary, this study provides an efficient and reliable solution for fault detection in computer communication networks by introducing an improved neural network algorithm. This approach is expected to play an important role in real network operations and improve network stability and security, thus providing more reliable support for information communication in modern society.

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Section: Modelling and Solvingmentioning

confidence: 99%

Studying computer communication networks to identify errors using enhanced neural network algorithms

zhang

2024

Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023)

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“…The GA is a widely applied construction methodology that aims to optimize costs and time [28][29][30]. It showcases efficiency in swiftly attaining optimal solutions by utilizing objective functions for straightforward problem-solving [31].…”

Section: Genetic Algorithm-based Automated Assembly Planningmentioning

confidence: 99%

Building an Information Modeling-Based System for Automatically Generating the Assembly Sequence of Precast Concrete Components Using a Genetic Algorithm

Bae,

Cha,

Jiang

2024

Applied Sciences

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Facing a significant decrease in economic working processes, Off-Site Construction (OSC) methods have been frequently adopted in response to challenges such as declining productivity and labor shortages in the construction industry. Currently, in most OSC applications, the assembly phase is traditionally managed based on the personal experience and judgment of the site managers. This approach can lead to inaccuracies or omissions, particularly when dealing with a large amount of information on large, complex construction sites. Additionally, there are limitations in exploring more efficient and productive alternatives for rapidly adapting to changing on-site conditions. Given that the assembly phase significantly affects the OSC productivity, a systematic management approach is crucial for expanding OSC methods. Some initial studies used computer algorithms to determine the optimal assembly sequences. However, these studies often focused on geometrical characteristics, such as component weight or spatial occupancy, neglecting crucial factors in actual site planning, such as the work radius and component installation status. Moreover, these studies tended to prioritize the generation of initial assembly sequences rather than providing alternatives for adapting to evolving on-site conditions. In response to these limitations, this study presents a systematic framework utilizing a Building Information Modeling (BIM)–Genetic Algorithm (GA) approach to generate Precast Concrete (PC) component installation sequences. The developed system employs Genetic Algorithms to objectively explore diverse assembly plans, emphasizing the flexibility of accommodating evolving on-site conditions. Real on-site scenarios were simulated using this framework to explore multiple assembly plan alternatives and validate their applicability. Comprehensive interviews were conducted to validate the research and confirm the system’s potential contributions, especially at just-in-time-focused PC sites. Acknowledging a broader range of variables such as equipment and manpower, this study anticipates fostering more systematic on-site management within the context of a digitized construction environment. The proposed algorithm contributes to improving both productivity and sustainability of the construction industry by optimizing the management process of the off-site construction projects.

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“…BIM can be represented in different dimensions such as 4D BIM (time), 5D BIM (cost), 6D BIM (facility management), 7D BIM (sustainability) and 8D BIM (safety) (Hamil, 2021). It is a model-based process that enables AEC professionals to be more productive in the design, construction and operation of buildings and is considered as a powerful approach in the renovation sector (Nusen et al ., 2021; Volk et al ., 2014). The incompleteness of available data and information in the renovation sector could be reconciled in BIM to improve work efficiency (Joblot et al ., 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Integrating building information modeling with augmented reality: application and empirical assessment in building facility management

Khan

Panuwatwanich²,

Usanavasin³

2023

ECAM

Self Cite

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PurposeThis paper presents a developed BIMxAR application, an integration of building information modeling (BIM) with augmented reality (AR) linked with real-time online database to support the building facility management work. The primary aim of this research was to develop and empirically examine the applicability of a BIM-based AR (BIMxAR) application in building facility management.Design/methodology/approachThe BIMxAR application was developed and experimented with maintenance work of a university laboratory building. The experiment consisted of a comparison of supportive maintenance tasks performed using the traditional approach and the BIMxAR approach by 38 university students. The time taken to complete each task was recorded and analyzed using statistical analyses to compare the performance between the tasks completed using each approach.FindingsThe results indicated that the group using the BIMxAR application approach completed the tasks correctly in a significantly shorter time compared to that using the traditional approach. The findings supported the applicability of the developed BIMxAR application and the improvement of the building facility management tasks when using the proposed approach.Originality/valueThis paper presents a methodological approach in developing a mobile application that integrates BIM with AR for facility management work, leveraging real-time information exchange through a cloud-based platform. The paper also provides empirical evidence that demonstrates how the integration between BIM and AR could be achieved and implemented to help facilitate building maintenance tasks.

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Construction Planning and Scheduling of a Renovation Project Using BIM-Based Multi-Objective Genetic Algorithm

Cited by 24 publications

References 48 publications

Studying computer communication networks to identify errors using enhanced neural network algorithms

Studying computer communication networks to identify errors using enhanced neural network algorithms

Building an Information Modeling-Based System for Automatically Generating the Assembly Sequence of Precast Concrete Components Using a Genetic Algorithm

Integrating building information modeling with augmented reality: application and empirical assessment in building facility management

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