Purpose: This study, a research project, aims to examine the distinct characteristics of the Fourth Industrial Revolution (4IR), with a focus on construction. Following this examination, the paper presents a field study to evaluate the impact of the 4IR on the construction process.Design/methodology/approach: The first half of this project is dedicated to defining the 4IR by reviewing existing literature. The other half of the project presents a case study to demonstrate the concept of the 4IR and measure the effect of its application. To validate the defined concept of the 4IR, the study focuses on the following: autonomous system for producing drawings and robotics in construction.Findings: The intensive literature review revealed three unequivocal features of the 4IR: defined tasks, undefined tasks and improvement possibilities. The following case study showed that the incorporation of the three 4IR features resulted in improved productivity and efficiency during the construction of the podium for the Lotte World Tower. For example, the macro-based autonomous system achieved 5.52 shop drawings per hour, highlighting the potential impact of independent, autonomous machinery.Originality/value: The originality of this project stems from its attempt to quantify the effectiveness of applying autonomous technologies to a practical project. While previous works in this field have focused on system development and improvement, this paper presents an autonomous system at work in an actual project, in which junior engineers were able to be entirely replaced. The system was successful in independently creating numerous required shop drawings. The value of this analysis is to generate scientific evidence to evaluate the efficacy of the adoption of 4IR-oriented technologies.

There are numerous applications for building dimension data, including building performance simulation and urban heat island investigations. In this context, object detection and instance segmentation methods—based on deep learning—are often used with Street View Images (SVIs) to estimate building dimensions. However, these methods typically depend on large and diverse datasets. Image augmentation can artificially boost dataset diversity, yet its role in building dimension estimation from SVIs remains under-studied. This research presents a methodology that applies eight distinct augmentation techniques—brightness, contrast, perspective, rotation, scale, shearing, translation augmentation, and a combined “sum of all” approach—to train models in two tasks: object detection with Faster Region-Based Convolutional Neural Networks (Faster R-CNNs) and instance segmentation with You Only Look Once (YOLO)v10. Comparing the performance with and without augmentation revealed that contrast augmentation consistently provided the greatest improvement in both bounding-box detection and instance segmentation. Using all augmentations at once rarely outperformed the single most effective method, and sometimes degraded the accuracy; shearing augmentation ranked as the second-best approach. Notably, the validation and test findings were closely aligned. These results, alongside the potential applications and the method’s current limitations, underscore the importance of carefully selected augmentations for reliable building dimension estimation.

The potential benefits of applied radio-frequency identification (RFID)-aided systems have been studied by many construction research projects. After reviewing previous works, this paper presents a self-developed system with the focus of a specific case in Australia. The system enables efficient material tracking, aiming for productivity improvement in the case. Initial site observations confirmed the need for an object-oriented material tracking system based on the principles of lean thinking and logistics qualities. Subsequently, a system was designed to activate a proposed method for optimizing material management during the supplier phase of the mega construction project. The experiments in this paper finally present two main streams: (1) performance testing of the system; and (2) productivity measure. As a result of performance testing, the tracking system was successful in locating the distributed 23 tags within an average range of 3.40 m. Supported by this system, the field productivity measurements consisting of four trailer-loading works (two lead and two tail trailers) quantified the feasible benefit of its application. The RFID-aided system facilitated the improvement of work performance, resulting in 10.14 (lead) and 11.80% (tail) productivity improvement. This research project therefore validated the benefits of RFID application in a construction supply chain by measuring its true value via the productivity improvements.

Construction projects require the generation and communication of an ever-larger amount of information. Unfortunately, present project organisations do not foster proactive approaches to preventing costly production waste derived from communication problems. To address this, the effective exchange of information needs to be performed across all different parties, as the construction project commonly includes concurrent progresses along its supply chain. This article proposes a new concept that integrates feed-forward control with the principle of network science. Referred to as supply-chain information network, it aims to comprehend the correlations within a sequenced supply chain network, in order to identify the critical communication links supporting a preventive control. Its effectiveness was verified by applying the model to an actual case: the rebar supply chain from a multi-story building in Sydney, NSW, Australia. The analysis recognised four problematical players in the supply chain: (1) rebar factory linemen, (2) radio crew, (3) senior steel fixers and (4) steel fixers, based on four centrality measurements. Consequently, the experimental data confirm the hypothesis that proactive management of the supply chain will produce drastic improvements, achieving up to 23.3% reduction in waste time during placement. The relevance of this research lies in the operational introduction of network science to the construction supply chain, and its proactive culture resulted in significant improvements of process performance. In addition, the developed communication backbone suggests an actualised test bed of concurrent information exchanges that tried to escape the spatiotemporal limitation in a chained attribute in the construction industry.

The procedure of falsework inspection has the potential for improvements in accuracy and time efficiency. The current process of inspection is often time-consuming and there is a potential for human error that could lead to undesirable outcomes. This paper presents an innovative smart system with the purpose of achieving an automation in falsework inspection. The system is basically a combination of radio frequency identification (RFID) with a virtual three-dimensional (3D) model that is represented by the AutoCAD 360 drawing viewer on a mobile device. Member identification is to be addressed by the RFID component, while the virtual model is expected to enable more accurate positional identification of members within the entire falsework configuration. A mock-up formwork structure was assembled in order to test this developed system. RFID was found to identify members significantly faster (5.6 s as an average) than by inspecting manually (33.2 s as an average) during 20 demonstrations. Simultaneously, higher inspection accuracy of 96.2% compared to 91.5% was achieved by inspecting manually. However, the smart technology option for component positioning did not have expected results and was less efficient than the currently practiced method. This shows that there is a potential in implementing one of the previous technologies while the other still requires further development before it would be applied to the inspection process. The primary contribution of the research lies in demonstrating the true value of the tangible smart system in an actual situation. In short, it enabled showing its effectiveness during the quantification, but still needs more work in its positioning.

Project claim management is the central pillar of the overlapping areas of contract administration, contract law and building regulations. Delays caused by inefficiency of the procedures designed to avoid disputes emerge at the pre-project phase and continue during construction. The quantum of research addressing this issue is not immediately transferrable between jurisdictions, mainly due to local specificity of construction practices, contract and construction laws, as well as clients’ preferences. The primary aim of this study is to identify the underlying causes of disputes that have arisen in the NSW construction industry in the past two decades and to analyze the inter-relationships between the causes. To achieve this purpose, PLS-SEM quantitative models were utilized to study different factors influencing disputes. Through a detailed quantitative analysis of 230 cases, based on dispute frequencies, causes and effect analysis and the resultant loop cause diagrams, the dispute triggers, types, and root causes have been analyzed as the basis for developing a model to predict the future likelihood of disputes. Finally, 13 causes of disputes have been recognized as the main causal factors in the construction projects in NSW. This study also has shown that payment and reimbursement-related disputes are the most frequent in NSW construction, except for the last two years.