A Predictive Analytics Framework for Mobile Crane Configuration Selection in Heavy Industrial Construction Projects
Round 1
Reviewer 1 Report
Dear authors
I just had the chance to read your work. I think the topic is interesting and timely. To help you improve your paper, I listed a few suggestions below:
The research gap could be strengthened in the introductory section. The latest advances and limitations in the crane configuration selection should be more elaborated. The authors should provide more elements to justify the research problem.
The background section should be written with more supportive references. (e.g., Line 69.). Minor text editing (Line 120).
The research's novelty and innovative contributions could be better identified in the paper (in the introduction or methodology).
Kind Regards
Author Response
Point 1: The research gap could be strengthened in the introductory section. The latest advances and limitations in the crane configuration selection should be more elaborated. The authors should provide more elements to justify the research problem.
Response 1: Thank you for your comment. The following sections has been added to the mauscript.
In this regard, a fuzzy logic approach was proposed by Hanna (1999) to select the type of crane. In this method, the expert’s vague knowledge of the suitability of crane types in various project conditions is translated into fuzzy sets and fuzzy rules. A fuzzy inference engine can quantitatively identify the best crane type for a project based on the description of the project’s condition that an expert provided in words[8]. This system is not capable of performing the crane model selection for a specific task.
LOCRANE was developed by Warszawski (1990) as a test case for applying the expert system methodology to construction planning tasks. It was limited to the selection of cranes for a given building. This knowledge base system cannot combine substitute transportation methods (e.g., equipment pumps, hoists, carts) with cranes for optimization and capturing interrelationships between crane selection and other construction tasks. CRANE ADVISER has been developed by Al-Hussein (1995) to integrate a knowledgebase and algorithm programs to assist in the crane selection for high-rise building projects. Expert knowledge, heuristics, and rules of thumb related to crane selection are contained in a knowledge-based module.
An expert system, entitled NEXPERT, was combined with geographical information system by Varghese (1992) to optimize the route selection when large objects are moving from the pick to set location to optimize the route selection. Wind speed, rental charges, lift radii, weights, and dimensions of the heaviest load have been considered in SELECTCRANE. This system was developed by Hanna (1994) to recommend the type of crane to the user. IntelliCRANES, which can assist with both crane type and model, was developed by Sawhney and Mund (2001). The system mainly includes two modules: (1) a neural network-based crane type selection module and (2) a knowledge-based expert system module for model selection. This system has some limitations, such as the number of cranes that can be selected as output is limited to only eight, and simultaneous crane selection for more than one crane for a construction site is not possible (Mund 1999).
Another system developed by Sohn et al. (2014) for tower crane selection considers all the costs used in the economic analysis. It converts them into the net present value for an accurate comparison. The minimum cost solution, the lateral support structure, the foundation design, and the individual components required are the final output of this system. HELPS2 is a system that can assist in overall lift planning from the preliminary planning stage to the detailed planning stage, final evaluation, and selection. This system was developed by Hornady et al. (1992), and the outputs increase in detail as the lifting plan evolves. HELPS2 optimizes the lifting plan according to the objectives of cost, reliability, safety, and performance. However, it still depends on the user to choose step by step (Hornaday et al. 1993).
Moselhi et al. (2004) developed a 3D modeling crane selection system that searches in its database for all technically feasible cranes according to the module’s dimension, weight, and location. The cranes retrieved from the databases can satisfy the specified clearance between the crane, the lift, and the adjacent buildings (Moselhi, Alkass, and Al-Hussein 2004). A 3D computer-aided application was developed by Wu et al. (2011) to select mobile cranes for heavy lifts. This application can consider the lifting capacity, clearance between the boom or jib to equipment, and ground bearing pressure. Although this system can find all the feasible cranes, it does not specify which one is the optimum solution, and the user needs to select the crane manually.
Point 2: The background section should be written with more supportive references. (e.g., Line 69.). Minor text editing (Line 120).
Response 2: Thank you for your suggestion. More supporting documents have been added
Point 3: The research's novelty and innovative contributions could be better identified in the paper (in the introduction or methodology).
Response 1: Thank you for your comment. The following sections has been added to the mauscript.
2.3 Goal and Objectives
This research aims to develop a neural network algorithm (Multilayer Perceptron Neural Network (MLP)) for mobile crane selection in heavy industrial construction projects to eliminate the limitations of the current mobile crane selection methods such as low accuracy, high processing time. The primary objectives of this research are:
Develop an artificial neural network based on historical data to predict the optimum mobile crane configurations for industrial projects.
Develop an interface to enable the user to interact with the developed applications.
Reviewer 2 Report
The manuscript is relevant to the field of knowledge and fits the profile of the journal (special issue)
Full understanding of the presented content requires reading the previous articles by the authors. It cannot be considered a mistake, because the content presented in the article is a continuation of many years of work of the authors. However, a reduction in the number of self-citations should certainly be considered.
At the same time, I suggest reducing the number of citations of older literature, and re-analyzing the latest literature (not older than 5 years).
The presented manuscript combines various scientific methods to solve the presented problem in a remarkable way, which is its strong point.
Planning the work of cranes is not of interest to the broad scientific community, nevertheless, the scientific methods used in the manuscript and their combination should be of interest to a wide range of readers.
In order to authenticate the research, please indicate in which project the crane configurator was used.
Author Response
Point 1: Full understanding of the presented content requires reading the previous articles by the authors. It cannot be considered a mistake, because the content presented in the article is a continuation of many years of work of the authors. However, a reduction in the number of self-citations should certainly be considered.
Response 2: Thank you for your comment. More supporting documents have been added to the paper and some the citetions have been modified to reflect your suggestions.
Point 2: I suggest reducing the number of citations of older literature, and re-analyzing the latest literature (not older than 5 years).
Response 2: Thank you for your comment. The following sections have been added to the mauscript.
A three-dimensional-based crane evaluation system to support the selection of crane and plan the crane lift schedule during the crane lift was developed in 2017. This system can design, verify, and simulate three-dimensional (3D) visualization of mobile crane operation. Moreover, this system can be utilized for better collaboration among project stakeholders [17]. An integrated decision support model was proposed by Han et. al (2018). For the purpose of determining the feasible crane type and model that will lead to the most efficient operation, the authors combined the conventional crane model selection methods with a 3D simulation and a crane selection matrix. Although the application of the proposed methodology is generic, the process is not fully automated and for each project the weights need to be assigned by the participants[18].
Point 3: In order to authenticate the research, please indicate in which project the crane configurator was used.
Response 3: Thank you for your comment. The application has been tested on previous heavy industrial projects in Canada, and the results in the paper are based on those projects. However, it is in the process of integrating with the current heavy lift planning system used by PCL Heavy Industrial Management Inc.
