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This paper provides an overview of a recently developed system for selecting and locating mobile cranes on construction sites. The proposed system provides direct help on two fronts: cost and time savings, and improved safety arrangements. The system has a number of interesting features: a relational database designed to store the cranes' geometry‐related variables and to present them using powerful graphics; a selection module supported by an algorithm designed to satisfy geometrical requirements and necessary clearances, accounting for site constraints and lift configurations; and 3D animation to facilitate the planning of crane operations. The system provides a near‐optimum selection of crane lift configurations, considering available cranes. This paper focuses mainly on case examples to demonstrate and to illustrate the use and capabilities of the developed system. Two actual cases, featuring different site constraints and lift configurations, are presented. In these cases, cranes were selected and their operations planned using the developed system. The findings of the two cases are discussed and the benefits of the proposed methodology are highlighted.

This paper presents a newly developed algorithm for selecting and locating mobile cranes on construction sites. The algorithm is incorporated into a computer system that integrates a selection module and three databases, dedicated respectively, for cranes, rigging equipment, and projects’ information. This paper focuses primarily on the selection module and its algorithm to support an efficient search for most suitable crane configurations and their associated lift settings. Data pertinent to crane lift configurations and settings are retrieved from the databases and processed to determine the near optimum selection of a crane configuration. The developed selection module features powerful graphics capabilities and a practical user‐friendly interface, designed to facilitate the considerations of user imposed lift and site constraints. The selection algorithm has been implemented within the crane selection module using MS‐Visual Basic programming language. A case example is presented in order to demonstrate the use of the developed selection module and to illustrate its essential features.

In regarding to operational efficiency and safety improvements, multiple tower crane service scheduling problem is one of the main problems related to tower crane operation but receives limited attention. The current work presents an optimization model for scheduling multiple tower cranes' service with overlapping areas while achieving collision-free between cranes.

The cooperative coevolutionary genetic algorithm (CCGA) was proposed to solve this model. Considering the possible types of cross-tasks, through effectively allocating overlapping area tasks to each crane and then prioritizing the assigned tasks for each crane, the makespan of tower cranes was minimized and the crane collision avoidance was achieved by only allowing one crane entering the overlapping area at one time. A case study of the mega project Daxing International Airport has been investigated to evaluate the performance of the proposed algorithm.

The computational results showed that the CCGA algorithm outperforms two compared algorithms in terms of the optimal makespan and the CPU time. Also, the convergence of CCGA was discussed and compared, which was better than that of traditional genetic algorithm (TGA) for small-sized set (50 tasks) and was almost the same as TGA for large-sized sets.

This paper can provide new perspectives on multiple tower crane service sequencing problem. The proposed model and algorithm can be applied directly to enhance the operational efficiency of tower cranes on construction site.

The crane plays an essential role in modern construction sites as it supports numerous operations and activities on-site. Additionally, the crane produces a big amount of data that, if analyzed, could significantly affect productivity, progress monitoring and decision-making in construction projects. This paper aims to show the usability of crane data in tracking the progress of activities on-site.

This paper presents a pattern-based recognition method to detect concrete pouring activities on any concrete-based construction sites. A case study is presented to assess the methodology with a real-life example.

The analysis of the data helped build a theoretical pattern for concrete pouring activities and detect the different phases and progress of these activities. Accordingly, the data become useable to track progress and identify problems in concrete pouring activities.

The paper presents an example for construction practitioners and researcher about a practical and easy way to analyze the big data that comes from cranes and how it is used in tracking projects' progress. The current study focuses only on concrete pouring activities; future studies can include other types of activities and can utilize the data with other building methods to improve construction productivity.

The proposed approach is supposed to be simultaneously efficient in terms of concrete pouring detection as well as cost-effective. Construction practitioners could track concrete activities using an already-embedded monitoring device.

While several studies in the literature targeted the optimization of crane operations and of mitigating hazards through automation and sensing, the opportunity of using cranes as progress trackers is yet to be fully exploited.

This research is aimed at predicting tower crane accident phases with incomplete data.

The tower crane accidents are collected for prediction model training. Random forest (RF) is used to conduct prediction. When there are missing values in the new inputs, they should be filled in advance. Nevertheless, it is difficult to collect complete data on construction site. Thus, the authors use multiple imputation (MI) method to improve RF. Finally the prediction model is applied to a case study.

The results show that multiple imputation RF (MIRF) can effectively predict tower crane accident when the data are incomplete. This research provides the importance rank of tower crane safety factors. The critical factors should be focused on site, because the missing data affect the prediction results seriously. Also the value of critical factors influences the safety of tower crane.

This research promotes the application of machine learning methods for accident prediction in actual projects. According to the onsite data, the authors can predict the accident phase of tower crane. The results can be used for tower crane accident prevention.

Previous studies have seldom predicted tower crane accidents, especially the phase of accident. This research uses tower crane data collected on site to predict the phase of the tower crane accident. The incomplete data collection is considered in this research according to the actual situation.

The site layout has a significant impact on the efficiency of construction operations. Planning an effective site layout partly involves identifying and positioning temporary facilities such as tower cranes and areas on the jobsite for materials storage. This study proposes an approach to optimizing the type and location of the tower crane and material supply point on construction sites.

The problem is formulated into an integer linear programming (ILP) model considering the total cost of material transportation as the objective function and site conditions as constraints. The efficacy of the approach is demonstrated by finding the optimum site layout for a numerical example. The proposed model is validated and verified using two methods.

Results indicate that the proposed model successfully identifies the type and location of the tower crane and the location of material supply point, leading to approximately 20% cost reduction compared with when such features of a site layout are decided solely based on experience and educated guesses of the construction manager.

The primary contribution of this study is to present a modified linear mathematical model for site layout optimization that exhibits improved performance compared with previous models. The type and location of the tower crane and the material supply point as decision variables are extracted directly from solving the proposed model. The proposed model will help enhance time and cost efficiency on construction sites.

Crane plays a very important role in national economy with greatly reduced labor intensity, improved production efficiency and promoted social development as an indispensable auxiliary tool and process equipment. Therefore, its energy consumption becomes an unavoidable topic and in fact, energy consumption of crane is very huge. It has been proved to be the most cost-effective way for reducing energy consumption to establish and implement new energy efficiency standard. Thus, it is necessary to analyze and evaluate the energy efficiency for overhead crane so as to propose a new energy efficiency standard. The paper aims to discuss these issues.

In this paper, four kinds of energy consumption sources of overhead crane is considered, based on which, an energy efficiency grading model for overhead crane based on BP neural network is proposed. Second, DS evidential theory is analyzed and based on it, an energy efficiency evaluation model based on BP neural network and DS evidential theory is proposed. The evaluation procedure is discussed in detail. Then, a case is demonstrated how the evaluation is carried out.

If overhead cranes with different energy consumptions need to be graded according to energy efficiency, the criterions to establish the energy efficiency labels for overhead cranes is proposed in this paper.

The research results can provide energy efficiency standard proposal of overhead crane for relative departments to monitor the design, manufacturing and use of overhead crane.

An energy efficiency grading model for overhead crane based on BP neural network is proposed. An energy efficiency evaluation model based on BP neural network and DS evidential theory is proposed.

This study aims to show how laser scanning data can be utilised to quantitatively assess “blind lifts” with respect to their rate and spatial distribution.

This study employed time study of crane cycles for quantitative measuring of the crane’s work periods in dead areas and mapping the crane operator’s field of view and developing a model that allows the spatial analysis of blind lifts.

This study found a discrete geometric laser scan-based model that is capable of locating and quantifying the visible and invisible zones from the crane operator’s cabin; 28 per cent of the analyzed crane’s work area represented by the model were found to be invisible, which corresponds fairly to 35 per cent of the half-cycles measured manually that were found to involve blind lifting; the range of blind lifts duration derived from the spatial information-based model was 50 to 84 per cent, which is in excellent correspondence with the 54 per cent to 82 per cent range obtained from the time unit-based analysis.

The laser-based model and the ensuing analyses are limited to the type of buildings whose envelope can practically be represented by the vertical extrusion of their footprint.

The practical implications of the study are reduction of blind lifts as a factor when selecting the location of the crane and staging areas; more effective preplanning of signallers positioning; and ad hoc consideration of analysed dead space for various lift task-based decision-making during construction.

This study demonstrates the ability to capture the geometric relations that characterise the work scene around the tower crane by harnessing the increasingly available laser technology and correlates the results of the manual observations with those obtained from the laser-based model.

A problematic issue for new approaches to prefabricated timber construction is simply that there is insufficient productivity measurement data to assist estimation of resource usage, speed onsite and best practice. A lack of information potentially results in increased pricing behaviour which may slow the uptake of prefabricated construction. The purpose of this paper is to measure installation productivity onsite for prefabricated timber floor cassette panels and develop sufficient understanding of the process to suggest improved practices.

A time and motion approach, paired with time-lapse photography was used for detailed capture of prefabricated cassette flooring installation processes onsite. An emphasis was placed on work flow around crane cycles from three case study projects. Time and date stamping from 300 crane cycles was used to generate quantitative data and enable statistical analysis.

The authors show that crane cycle speed is correlated to productivity including gross and net crane time scenarios. The latter is refined further to differentiate uncontrolled outlying crane cycles from normally distributed data, representing a controlled work process. The results show that the installation productivity rates are between 69.38 and 123.49 m²/crane-hour, based on normally distributed crane cycle times. These rates were 10.8–26.1 per cent higher than the data set inclusive of outlier cycles. Large cassettes also proved to be more productive to place than small.

The contribution of this research is the focus on cranage as the lead resource and the key unit of measure driving installation productivity (in cassette flooring prefabricated construction), as distinct from past research that focuses on labour and craft-based studies. It provides a different perspective around mechanisation, for resourcing and planning of work flow. Crane cycles provide a relatively easy yet reliably repeatable means for predicting productivity. The time-lapse photographic analysis offers a high degree of detail, accuracy and objectivity not apparent in other productivity studies which serves to enable quantitative benchmarking with other projects.

The purpose of this study is to monitor the progress of construction activities in an automated way by using sensor-based technologies for tracking multiple resources that are used in building construction.

An automated on-site progress monitoring approach was proposed and a proof-of-concept prototype was developed, followed by a field experimentation study at a high-rise building construction site. The developed approach was used to integrate sensor data collected from multiple resources used in different steps of an activity. It incorporated the domain-specific heuristics that were related to the site layout conditions and method of activity.

The prototype estimated the overall progress with 95% accuracy. More accurate and up-to-date progress measurement was achieved compared to the manual approach, and the need for visual inspections and manual data collection from the field was eliminated. Overall, the field experiments demonstrated that low-cost implementation is possible, if readily available or embedded sensors on equipment are used.

Previous studies either monitored one particular piece of equipment or the developed approaches were only applicable to limited activity types. This study demonstrated that it is technically feasible to determine progress at the site by fusing sensor data that are collected from multiple resources during the construction of building superstructure. The rule-based reasoning algorithms, which were developed based on a typical work practice of cranes and hoists, can be adapted to other activities that involve transferring bulk materials and use cranes and/or hoists for material handling.