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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.

A tower crane mainly ensures the success or efficiency of building construction. Fatigue crack analysis is important for tower crane components to prevent any accidents to workers in construction sites caused by component failure and to ease the maintenance or replacement of failed components. This work aimed to characterise the damage of failed components, analyse the relationship between the metal magnetic memory (MMM) result and the damage of failed components, and to validate the relationship between MMM and finite element analysis (FEA).

MMM was used in this work to detect any irregularities or early failure on the basis of the high stress concentration zone of ferromagnetic steel using magnetic flux leakage. Magnetic flux leakage was used on the MMM device to achieve the first objective using the MMM system by detecting the irregularities. The results of MMM analysis were validated through comparison with FEA results by determining their relationship.

MMM results show that the position of defects on the tower crane pulley is within the stress area shown on FEA.

Hence, MMM method is a potential tool in monitoring failure mechanism in construction site.

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.

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 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.

This discussion treats relational analysis as an alternative to the ‘categorical’ view of syntactic structures in indexing. It is suggested that the relational characterization of syntactic structures by reference to the meaning of the spaces between constituent terms may point the way to classification structures which are stable to new knowledge and discipline‐independent. Conversely the meaning‐protecting role of disciplinary domains has in the past relegated relational ideas to a peripheral significance in classification structures. General properties of syntactic strings, logical articulation, disarticulation, and linearization of branching relationships are discussed, together with the role of relational symbolism as noise in significant‐word based searches. Attention is next given to certain derivative relations which arise out of an inclusion relation between an isolated concept and the relation‐linked combination of which it forms a part. One class of these derivative relations is an explicit syntactic relation and its affiliation to other syntactic relations may throw light on the little understood nature and development of the content of personality facets.

This study aims to thoroughly examine the trends and developments of crane layout planning (CLP) in the construction field and reveal future research directions for modular integrated construction (MiC).

Through a rigorous systematic mixed-review methodology that integrates bibliometric, scientometric and qualitative analysis, this study explored the crane layout research trend; the scientometric analysis of journal sources and keywords occurrence network; the research contributions and links between influential countries; the classification of research articles based on the type of problems and solution approaches; the qualitative analysis of existing findings and research gaps; and the future research direction for CLP in MiC.

This study found five categories under the CLP domain, namely, crane selection, crane location, integrated crane selection and location, integrated crane location and allocation of supply points and hybrid problems. The major research approaches used to solve CLP is optimization (43%), visualization (23%), decision support systems (16%), simulation (11%) and qualitative techniques (7%). The possible future research directions include artificial intelligence-based models, multi-crane locations, CLP for MiC re-use, dynamic models representing real-life scenarios and building information modeling-based virtual reality models.

Through a mixed-review methodology, this study provides a comprehensive analysis of problem settings and solution methods of CLP while mitigating the subjectivity of traditional review methods. Also, it presents a repertoire on CLP and illuminates future directions for seasoned researchers in the context of MiC.

Most of the 3D printing machines do not comply with the requirements of on-site, large-scale multi-story building construction. This paper aims to propose the conceptualization of a tower crane (TC)-based 3D printing controlled by artificial intelligence (AI) as the first step towards a large 3D printing development for multi-story buildings. It also aims to overcome the most important limitation of additive manufacturing in the construction industry (the build volume) by exploiting the most important machine used in the field: TCs. It assesses the technology feasibility by investigating the accuracy reached in the printing process.

The research is composed of three main steps: firstly, the TC-based 3D printing concept is defined by proposing an aero-pendulum extruder stabilized by propellers to control the trajectory during the extrusion process; secondly, an AI-based system is defined to control both the crane and the extruder toolpath by exploiting deep reinforcement learning (DRL) control approach; thirdly the proposed framework is validated by simulating the dynamical system and analysing its performance.

The TC-based 3D printer can be effectively used for additive manufacturing in the construction industry. Both the TC and its extruder can be properly controlled by an AI-based control system. The paper shows the effectiveness of the aero-pendulum extruder controlled by AI demonstrated by simulations and validation. The AI-based control system allows for reaching an acceptable tolerance with respect to the ideal trajectory compared with the system tolerance without stabilization.

In related literature, scientific investigations concerning the use of crane systems for 3D printing and AI-based systems for control are completely missing. To the best of the authors’ knowledge, the proposed research demonstrates for the first time the effectiveness of this technology conceptualized and controlled with an intelligent DRL agent.

The results provide the first step towards the development of a new additive manufacturing system for multi-storey constructions exploiting the TC-based 3D printing. The demonstration of the conceptualization feasibility and the control system opens up new possibilities to activate experimental research for companies and research centres.

Precast wall lifting during prefabricated building construction faces multiple non-lean problems, such as inaccurate lifting-time estimation, unreasonable resource allocation and improper process design. This study aims to identify the pathways for improving lifting performance to advance lean construction of prefabricated buildings.

This study developed a methodological framework that integrates the discrete event simulation method, the elimination, combination, rearrangement and simplification (ECRS) technique and intelligent optimization tool. Two schemes of precast wall lifting, namely, the enterprise's business as usual (BAU) and enterprise-leading (EL) schemes, were set to benchmark lifting performance. Furthermore, a best-practice (BP) scheme was modeled from the perspective of lifting activity ECRS and resource allocation for performance optimization.

A real project was selected to test the effect of the methodological framework. The results showed that compared with the EL scheme, the BP scheme reduced the total lifting time (TLT) by 6.3% and mitigated the TLT uncertainty (the gap between the maximum and minimum time values) by 20.6%. Under the BP scheme, increasing the resource inputs produces an insignificant effect in reducing TLT, i.e. increasing the number of component operators in the caulking subprocess from one to two only shortened the TLT by 3.6%, and no further time reduction was achieved as more component operators were added.

To solve non-lean problems associated with prefabricated building construction, this study provides a methodological framework that can separate a typical precast wall lifting process into fine-level activities. Besides, it also identifies the pathways (including the learning effect mitigation, labor and machinery resource adjustment and activities’ improvement) to reducing TLT and its uncertainty.

Construction safety has been a long-term problem in the development of the construction industry. An increasing number of smart construction sites have been designed using different techniques to reduce injuries caused by construction accidents and achieve proactive risk control. However, comprehensive smart construction site safety management solutions and applications have yet to be developed. Thus, this study proposes a smart construction site framework for safety management.

A safety management system based on a cyber-physical system is proposed. The system establishes risk data synchronization mapping between the virtual construction and physical construction sites through scene reconstruction design, data awareness, data communication and data processing modules. Personnel, mechanical and other risks on site will be warned and controlled.

The results of the case study have proved the management benefits of the system. On-site workers gradually realized that they should enter the construction site based on the standard process. And the number of people close to the construction hazard areas decreased.

There are some limitations in the technology of smart construction site. The modeling speed can be faster, the data collection can be timelier, and the identification of unsafe behavior can be integrated into the system. Construction quality and efficiency issues in a virtual construction site will also be solved in further research.

In this paper, this system is actually applied in the mega project management process. More practical projects can use the management ideas and method of this paper to ensure on-site safety.

This study is among the first attempts to build a complete smart construction site based on CPS and apply it in practice. Personnel, mechanical, components, environment information will be displayed on the virtual construction site. It will greatly promote the development of the intellectualized construction industry in the future.