Risk assestment of housing reconstruction project community-based construction after the earthquake

Boy, Wendi; Imani, Rafki; Chari, Mayozi; Purba, Widiawati; Melasari, Jihan

doi:10.1088/1742-6596/1339/1/012001

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…Based on the system's high-speed camera's acquisition of image data from all angles of the realtime scenes of the construction site (as shown in Figure 3), the DLR-P system automatically analyzes the real-time scenes of the project construction site and obtains the actual progress in the form of point cloud models for each of the three construction processes as shown in Figure 4. By crossreferencing the point cloud model with the idea BIM point cloud, the difference between the actual construction progress and the expected ideal progress is automatically calculated [33].…”

Section: Simulation Resultsmentioning

confidence: 99%

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Construction of the 3D Reconstruction System of Building Construction Scene Based on Deep Learning

Zhou

2021

Scientific Programming

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The increasing complexity and enormity of construction projects, as well as the fact that the actual operation of construction schedule management still mainly relies on traditional manual management methods, have led to low efficiency of construction schedule management and caused many construction projects to have cost overruns and legal disputes due to schedule delays. Existing 3D reconstruction algorithms often lead to significant voids, distortions, or blurred parts in the reconstructed 3D models, while the machine learning-based 3D reconstruction algorithms are often only to reconstruct simple separated objects and represent them as 3D boxes. A novel architecture of semisupervised 3D reconstruction algorithm is proposed. The algorithm iteratively improves the quality of the original 3D reconstruction model by training a generative adversarial network model to a converged state. Only the prior observed 2D images are required as weakly supervised samples, without any dependence on prior knowledge of the 3D structure shape or reference observations. Experimental results show that this algorithmic framework has significant advantages over the current state-of-the-art 3D reconstruction methods on the standard 3D reconstruction test set.

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Section: Simulation Resultsmentioning

confidence: 99%

“…e representative algorithm of this class is the 3D Recurrent Reconstruction Neural Network (3D-R2N2) [33] algorithm, which uses a deep CNN to learn the mapping relationship between the observed 2D image and the corresponding 3D shape of the target object from a large training dataset.…”

Section: Related Workmentioning

confidence: 99%

Construction of the 3D Reconstruction System of Building Construction Scene Based on Deep Learning

Zhou

2021

Scientific Programming

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“…In the 3D model, more nonmathematical data like cost boundaries, market data, and cost change variables can be added. This implies that the development interaction or project plan can measure up according to the perspective of designing expenses, lessening configuration changes, and making the task speculation more precise and sensible [39][40][41][42][43].…”

Section: Simulation Outcomementioning

confidence: 99%

Construction of 3D Reconstruction System for Building Construction Scenes Based on Deep Learning and IoT

Wang

2022

Wireless Communications and Mobile Computing

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The developing intricacy and extent of building projects, as well as the way that construction schedule management is still mostly done by hand, has resulted in low efficiency in construction schedule management, resulting in cost overruns and legal disputes as a result of schedule delays in many projects. Existing 3D reconstruction algorithms frequently result in large there may be holes, distortions, or hazy regions in remade 3D models, but AI-based 3D remaking strategies regularly reestablish straightforward isolated parts and portray them as 3D boxes. Accordingly, these algorithmic systems are generally not sufficient for certifiable use. The primary objective of this paper is to apply the creation ill-disposed network strategy to 3D recreation works on the nature of the underlying 3D recreation model via preparing a generative ill-disposed network model to a joined state. As solo examples, just recently noticed 2D pictures are required, with no dependence on earlier information on the 3D hidden shape or reference insights. Test results show that this algorithmic structure outflanks present status of-the-workmanship 3D reproduction approaches on an average 3D recreation test set. On the common 3D remaking test set, exploratory outcomes uncover that this algorithmic structure beats the current cutting edge 3D reproduction techniques.

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“…Pembangunan proyek konstruksi merupakan salah satu kegiatan yang memiliki risiko terjadinya kecelakaan kerja yang cukup tinggi [1]. Apabila bahaya tersebut tidak dicegah, maka memerlukan biaya yang lebih besar dalam pemberian kompensasi bagi pekerja yang mengalami kecelakaan [2].…”

Section: Pendahuluanunclassified

Analysis of the Occupational Health and Safety Management System (SMK3) in the Santika Premiere Padang Hotel Development Project

Imani

Sari

Pratama

2021

jcivil

Self Cite

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Occupational safety and health are the main means of preventing accidents, disability and death as a result of work accidents. K3 aims to prevent, reduce, and even eliminate the risk of work accidents (zero accident). The application of this concept should not be considered as an effort to prevent work accidents and occupational diseases that cost the company a lot of money, but should be considered as a form of long-term investment that will provide abundant benefits in the future. This study aims to determine the implementation of the implementation and the level of success, as well as to determine the factors causing the delay in the implementation of the occupational safety and health management system (SMK3) in the construction project of the Hotel Santika Premiere Padang.The stage of this research is the study of various existing literatures to determine the variables to be used. The next stage is designing research questionnaires and then collecting data by distributing questionnaires directly to the field. The data obtained from the questionnaire will be analyzed for validity and reliability tests using the Statistical Package for Social Science (SPSS) software.The results of this study indicate that the success of the implementation of the occupational safety and health management system (SMK3), which is taken from the average sum of all variables, is 98.5%. So that it can be concluded that the level of achievement of the successful implementation of SMK3 in the construction project of the Hotel Santika Premiere Padang is based on the determination of the success value written in the Minister of Permenaker Number: 05/MEN/1996, eligible for a certificate and a gold flag. This is in accordance with category number 3, namely "where the level of achievement of 85-100% is given a certificate and a gold flag".

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Risk assestment of housing reconstruction project community-based construction after the earthquake

Cited by 4 publications

References 6 publications

Construction of the 3D Reconstruction System of Building Construction Scene Based on Deep Learning

Construction of the 3D Reconstruction System of Building Construction Scene Based on Deep Learning

Construction of 3D Reconstruction System for Building Construction Scenes Based on Deep Learning and IoT

Analysis of the Occupational Health and Safety Management System (SMK3) in the Santika Premiere Padang Hotel Development Project

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