This article proposes a methodology to measure the productivity of a construction site through the analysis of tower crane data. These data were obtained from a data logger that records a time series of spatial and load data from the lifting machine during the structural phase of a construction project. The first step was data collection, followed by preparation, which consisted of formatting and cleaning the dataset. Then, a visualization step identified which data was the most meaningful for the practitioners. From that, the activity of the tower crane was measured by extracting effective lifting operations using the load signal essentially. Having used such a sampling technique allows statistical analysis on the duration, load, and curvilinear distance of every extracted lifting operation. The build statistical distribution and indicators were finally used to compare construction site productivity.
This paper provides a design of the Information System architecture to support a connected construction site. In order to master the diversity and the complexity of construction site processes, theories are needed that separate the stable essence of the smart construction site from the variable way in which it is realized and implemented. For that, construction site processes were mapped before linking each data path with the existing technological tools using correspondence matrixes. The results enable the definition of a proper system able to deal with the resources allocated to the construction process functionalities. The main challenge faced in this research was to identify which pertinent data is needed that activates the resources to complete each given construction task.
While the design practice in the architecture, engineering, and construction (AEC) industry continues to be a creative activity, approaching the design problem from a perspective of the decision-making science has remarkable potentials that manifest in the delivery of high-performing sustainable structures. These possible gains can be attributed to the myriad of decision-making tools and technologies that can be implemented to assist design efforts, such as artificial intelligence (AI) that combines computational power and data wisdom. Such combination comes to extreme importance amid the mounting pressure on the AEC industry players to deliver economic, environmentally friendly, and socially considerate structures. Despite the promising potentials, the utilization of AI, particularly reinforced learning (RL), to support multidisciplinary design endeavours in the AEC industry is still in its infancy. Thus, the present research discusses developing and applying a Markov Decision Process (MDP) model, an RL application, to assist the preliminary multidisciplinary design efforts in the AEC industry. The experimental work shows that MDP models can expedite identifying viable design alternatives within the solutions space in multidisciplinary design while maximizing the likelihood of finding the optimal design.
The main objective of this study was external facade deviation analyses applied to prefabricated off-site concrete modules. This article is decomposed into three main parts: the first part is dedicated to the introduction and to the research background. The second part explains and details the construction project, the off-site factory, the modules as well as the use of a terrestrial laser scanner. A Framework and a data acquisition layout are also exposed. The third part elaborates, in addition to the discussion and study limitations, the main key results which were obtained. The bulging effect on the bottom half of the modules can be explained by the fact that, the greater the quantity of concrete is poured, the more the inside pressure of the formwork increases, exposing the mould’s structure to additional deformations.
PurposeThe 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.Design/methodology/approachThis 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.FindingsThe 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.Research limitations/implicationsThe 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.Practical implicationsThe 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.Originality/valueWhile 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.
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