Estimation of Job-Site Work Progress through On-Site Monitoring

Abstract

Purpose This paper reports on the development of intelligent probabilistic models for real-time estimation of construction progress, which operate on the basis of a continuous data flow collected by monitoring networks deployed on-site. Several authors listed the advantages that would be provided by the availability of such models, like project performance and quality control, timely onsite inspections, better control of health and safety prescriptions against job injuries and fatalities. The findings reported in this paper represent a feasibility study and preliminary examples of Bayesian Networks, which are able to infer the work progress attained at every step, starting from real-time tracks of the construction site activities. Activity tracks are represented as a set of state variables figuring out workers' effort, equipment and materials usage rates and other knowledge about the context. Method As estimations are always related to dynamic processes, Dynamic Object Oriented Bayesian Networks have been used to develop a set of first order Hidden Markov Models. Hence, the models are arranged as a sequence of time steps, where each time step propagates evidences collected by the site monitoring sensor network along the time line. The actual cumulative progress is computed as a function of the progress achieved in each time step. Models representing a number of typical tasks (external piping, onsite cast of reinforced concrete floor slab, walls erection, ceiling installation) for a real case of a construction site have been developed. Their structure has been designed as part of a general monitoring framework, covering all the phases from design to execution, where BIM design, monitoring systems, methodological process innovations, intelligent inferences and advanced visualization are combined. Results & Discussion The networks have been developed and validated through data collected from a real case, and they have been shown to be able to infer work progress, the accuracy of which depends on the resolution and quality of the collected data.

Modelling construction scheduling constraints using shapes constraint language (SHACL)

“…For instance, Han and Golparvar-Fard (2017) have stated that the current methods cannot document field issues for further analysis as the 4D BIM's "Model Breakdown Structure typically does not match operational details or require creating complicated namespaces which, without visual representations, are difficult to communicate" (p. 1733). In the same context, Giretti et al (2012) had stated that they had to decompose the tasks into sub-tasks to determine causal relationships among the involved variables so the whole progress could be estimated since there is no linear dependence between the resources employed at every hour and the work progress. The level of detail of 4D BIM is often limited to the master planning or phase planning level and lacks the level of detail and linkage required in the six weeks look ahead level.…”

Proceedings of the 2019 European Conference on Computing in Construction

“…For instance, Han and Golparvar-Fard (2017) have stated that the current methods cannot document field issues for further analysis as the 4D BIM's "Model Breakdown Structure typically does not match operational details or require creating complicated namespaces which, without visual representations, are difficult to communicate " (p. 1733). In the same context, Giretti et al (2012) had stated that they had to decompose the tasks into sub-tasks to determine causal relationships among the involved variables so the whole progress could be estimated since there is no linear dependence between the resources employed at every hour and the work progress. The level of detail of 4D BIM is often limited to the master planning or phase planning level and lacks the level of detail and linkage required in the six weeks look ahead level.…”

Pervasive Sensor Network for Real Time Environmental Monitoring in Construction Sites

“…Hence, much research is focused on the aspects of cost-effective construction management, intelligent waste handling and improvement of safety at work (A. Giretti et al, 2012). First, setting up a cost-effective construction project management would include material management and inventory traceability which conceivably contribute to the construction process (Weisheng et al, 2011).…”