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1 – 3 of 3Mohammad Hosein Madihi, Ali Akbar Shirzadi Javid and Farnad Nasirzadeh
In traditional Bayesian belief networks (BBNs), a large amount of data are required to complete network parameters, which makes it impractical. In addition, no systematic method…
Abstract
Purpose
In traditional Bayesian belief networks (BBNs), a large amount of data are required to complete network parameters, which makes it impractical. In addition, no systematic method has been used to create the structure of the BBN. The aims of this study are to: (1) decrease the number of questions and time and effort required for completing the parameters of the BBN and (2) present a simple and apprehensible method for creating the BBN structure based on the expert knowledge.
Design/methodology/approach
In this study, by combining the decision-making trial and evaluation laboratory (DEMATEL), interpretive structural modeling (ISM) and BBN, a model is introduced that can form the project risk network and analyze the impact of risk factors on project cost quantitatively based on the expert knowledge. The ranked node method (RNM) is then used to complete the parametric part of the BBN using the same data obtained from the experts to analyze DEMATEL.
Findings
Compared to the traditional BBN, the proposed method will significantly reduce the time and effort required to elicit network parameters and makes it easy to create a BBN structure. The results obtained from the implementation of the model on a mass housing project showed that considering the identified risk factors, the cost overruns relating to material, equipment, workforce and overhead cost were 37.6, 39.5, 42 and 40.1%, respectively.
Research limitations/implications
Compared to the traditional BBN, the proposed method will significantly reduce the time and effort required to elicit network parameters and makes it easy to create a BBN structure. The results obtained from the implementation of the model on a mass housing project showed that considering the identified risk factors, the cost overruns relating to material, equipment, workforce and overhead cost were 37.6, 39.5, 42 and 40.1%, respectively. The obtained results are based on a single case study project and may not be readily generalizable.
Originality/value
The presented framework makes the BBN more practical for quantitatively assessing the impact of risk on project costs. This helps to manage financial issues, which is one of the main reasons for project bankruptcy.
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Hadi Mahamivanan, Navid Ghassemi, Mohammad Tayarani Darbandy, Afshin Shoeibi, Sadiq Hussain, Farnad Nasirzadeh, Roohallah Alizadehsani, Darius Nahavandi, Abbas Khosravi and Saeid Nahavandi
This paper aims to propose a new deep learning technique to detect the type of material to improve automated construction quality monitoring.
Abstract
Purpose
This paper aims to propose a new deep learning technique to detect the type of material to improve automated construction quality monitoring.
Design/methodology/approach
A new data augmentation approach that has improved the model robustness against different illumination conditions and overfitting is proposed. This study uses data augmentation at test time and adds outlier samples to training set to prevent over-fitted network training. For data augmentation at test time, five segments are extracted from each sample image and fed to the network. For these images, the network outputting average values is used as the final prediction. Then, the proposed approach is evaluated on multiple deep networks used as material classifiers. The fully connected layers are removed from the end of the networks, and only convolutional layers are retained.
Findings
The proposed method is evaluated on recognizing 11 types of building materials which include 1,231 images taken from several construction sites. Each image resolution is 4,000 × 3,000. The images are captured with different illumination and camera positions. Different illumination conditions lead to trained networks that are more robust against various environmental conditions. Using VGG16 model, an accuracy of 97.35% is achieved outperforming existing approaches.
Practical implications
It is believed that the proposed method presents a new and robust tool for detecting and classifying different material types. The automated detection of material will aid to monitor the quality and see whether the right type of material has been used in the project based on contract specifications. In addition, the proposed model can be used as a guideline for performing quality control (QC) in construction projects based on project quality plan. It can also be used as an input for automated progress monitoring because the material type detection will provide a critical input for object detection.
Originality/value
Several studies have been conducted to perform quality management, but there are some issues that need to be addressed. In most previous studies, a very limited number of material types were examined. In addition, although some studies have reported high accuracy to detect material types (Bunrit et al., 2020), their accuracy is dramatically reduced when they are used to detect materials with similar texture and color. In this research, the authors propose a new method to solve the mentioned shortcomings.
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Ahmed Nouh, Elsayed Elkasaby and Omnia Wageh
Innovative design and execution approaches are employed in infrastructure sectors and planning to enhance the integrated project delivery system, assure the sustainability of…
Abstract
Purpose
Innovative design and execution approaches are employed in infrastructure sectors and planning to enhance the integrated project delivery system, assure the sustainability of infrastructure projects, and meet the demands of the dynamic, changing environment. Delivery methods must incorporate new technologies. By combining digital technology, teamwork, and mass manufacturing, a greater degree of exceptional quality, sustainability, and resilience in the environment will be generated. As a result, a new approach does not rely on the reaction policy, but instead considers alternative scenarios and employs a simulation model to determine the best course of action.
Design/methodology/approach
In the paper, the system dynamics approach to construction management is validated in light of pertinent research. Additionally, it describes the difficulties facing the infrastructure projects' delivery system. Additionally, the strategy for system dynamics creation is described. This strategy includes a causal loop diagram, generates a stock-flow diagram, and simulates forecasts of model behavior over time. Next, the optimization model's validation process is used to create a system dynamics model for choosing the best infrastructure project delivery system project and controlling it to maximize sustainability, mass production, digital integration, and team integration. The dynamic complexity of project management is growing.
Findings
The primary goal is to present a system dynamics (SD) simulation to look at how well infrastructure projects perform in terms of choosing the best method for delivering infrastructure projects. One of the most ideal methods for delivering projects is integrated project delivery. An effective methodology for making strategic decisions on the choice of the best project delivery method. In order to enhance certain infrastructure project delivery system metrics for sustainability, mass production, digital integration, and team integration, the model included building strategy and sophisticated system dynamics simulation. According to the construction strategy, the outcomes have been satisfactory.
Originality/value
System dynamics research has been done to replicate the idea of contemporary construction in order to determine the best approach for delivering infrastructure. The government and decision-makers would benefit from understanding this research as they decide on the best delivery method for boosting the sustainability and productivity of infrastructure projects in Egypt.
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