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An Automated Mobile Crane Selection System for Heavy Industrial Construction Projects
Lifting heavy objects on the construction sites has always been a challenge for projects, since a great number of parameters are involved. Heavy lift planning has an influence on the whole project especially on cost, scheduling, and safety. Proper crane selection as a part of heavy-lift planning has been done by engineers using tabulated crane capacity charts for each configuration. Capacity charts are organized with details of the cranes, e.g., the length and the angle of the main boom, lifting radius, jib length and angle, and super-lift capacity, which makes the aforementioned crane selection process tedious. This paper proposes a heuristic approach that utilizes machine learning techniques and structured query language to select the proper crane for a lifting scenario. The approach takes into account the size and weight of all the modules in the project, boom and anti-two block clearance, lifting radius, dimension and weight of the equipment such as hook and rigging, and the cost of the crane usage. The proposed algorithm calculates the boom and anti-two block clearance in three-dimensional (3D) space rather than two dimensional (2D), which provides more accurate results. This approach enables an automated crane selection process that helps improve planning efficiency and accuracy. Moreover, the algorithm assigns a score to each configuration based on the user input and provide the user with the near optimum crane for the project. The parameters that affect the score of each configuration include the percentage capacity of the crane that has been utilized, Anti-Two Block clearance, boom clearance, monthly rent of the crane, mobilization and demobilization cost, project duration, and super-lift. The weight associated to each criterion has been determined through both running sensitivity analysis and consulting with lift engineering experts.
An Automated Mobile Crane Selection System for Heavy Industrial Construction Projects
Lifting heavy objects on the construction sites has always been a challenge for projects, since a great number of parameters are involved. Heavy lift planning has an influence on the whole project especially on cost, scheduling, and safety. Proper crane selection as a part of heavy-lift planning has been done by engineers using tabulated crane capacity charts for each configuration. Capacity charts are organized with details of the cranes, e.g., the length and the angle of the main boom, lifting radius, jib length and angle, and super-lift capacity, which makes the aforementioned crane selection process tedious. This paper proposes a heuristic approach that utilizes machine learning techniques and structured query language to select the proper crane for a lifting scenario. The approach takes into account the size and weight of all the modules in the project, boom and anti-two block clearance, lifting radius, dimension and weight of the equipment such as hook and rigging, and the cost of the crane usage. The proposed algorithm calculates the boom and anti-two block clearance in three-dimensional (3D) space rather than two dimensional (2D), which provides more accurate results. This approach enables an automated crane selection process that helps improve planning efficiency and accuracy. Moreover, the algorithm assigns a score to each configuration based on the user input and provide the user with the near optimum crane for the project. The parameters that affect the score of each configuration include the percentage capacity of the crane that has been utilized, Anti-Two Block clearance, boom clearance, monthly rent of the crane, mobilization and demobilization cost, project duration, and super-lift. The weight associated to each criterion has been determined through both running sensitivity analysis and consulting with lift engineering experts.
An Automated Mobile Crane Selection System for Heavy Industrial Construction Projects
Lecture Notes in Civil Engineering
Walbridge, Scott (editor) / Nik-Bakht, Mazdak (editor) / Ng, Kelvin Tsun Wai (editor) / Shome, Manas (editor) / Alam, M. Shahria (editor) / el Damatty, Ashraf (editor) / Lovegrove, Gordon (editor) / Azami, R. (author) / Lei, Z. (author) / Hermann, R. (author)
Canadian Society of Civil Engineering Annual Conference ; 2021
Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 ; Chapter: 9 ; 107-119
2022-05-30
13 pages
Article/Chapter (Book)
Electronic Resource
English
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