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Abbas Shahri (2016)
An Optimized Artificial Neural Network Structure to Predict Clay Sensitivity in a High Landslide Prone Area Using Piezocone Penetration Test (CPTu) Data: A Case Study in Southwest of SwedenGeotechnical and Geological Engineering, 34
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Challenges faced in the construction of 60 m deep diaphragm walls, with hydraulic grabs in central london
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Optimized developed artificial neural network-based models to predict the blast-induced ground vibrationInnovative Infrastructure Solutions, 3
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Planning and cost estimation engineers face great challenges to estimate construction productivity rate (PR) of diaphragm walls (DWs). A specific guideline to predict the construction of DWs in underground station is still not available. In order to overcome these challenges, the criteria affecting PR for the construction of DWs should be hence defined and the factors that impact the construction productivity of DWs should be well understood. Briefly and given the limitations of previous research work, we gathered and compiled a comprehensive key list of all criteria based on previous reports. This comprehensive key list was later reviewed by underground construction and specialized experts to define, identify and distinguish the most important criterion affecting the construction productivity of DWs in Egypt. We developed two questionnaire surveys to conduct these interviews with the experts. This obtained criterion was ranked and weighted using Simos’ procedure based on prioritizing the influencing criteria. Our findings concluded that the ground condition type/and characteristics, soil–machine interaction, machine type and model characteristics (grab/or cutter), breakdowns and de-sender efficiency are significantly influencing the excavation rate. Additionally, factors such as cage type (fiber/or steel), connection type (welding/or mechanical) and volume of steel fixing crew are crucial to identify the rate of installation cages. On the other side, the arrival time of concrete car mixer and panel volume were determined to significantly impact the concrete casting rate. In summary, this study presented a distinctive weighting and ranking methodology of the influencing criteria for the PR of DWs. Furthermore, this work provides an initial concept for many full-scale prediction applications to estimate an accurate construction productivity of DWs, particularly in the underground projects.
Innovative Infrastructure Solutions – Springer Journals
Published: Aug 28, 2020
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