Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A mixed review of the adoption of Building Information Modelling (BIM) for sustainability

A survey on modeling guidelines for quantity takeoff-oriented BIM-based design

Building-information-modeling enabled life cycle assessment, a case study on carbon footprint accounting for a residential building in China

Off-site construction (OSC) is known as an efficient construction method that could save time and cost, reduce waste of resources, and improve the overall productivity of projects. Coupled with digital technologies associated with the Industry 4.0 concept, OSC can offer a higher rate of productivity and safety. While there is a rich literature focusing on both OSC and Industry 4.0, the implementation of associated digital technologies in the OSC context has not been fully evaluated. This paper intends to evaluate the current literature of digital technology applications in OSC. Scientometric analyses and a systematic review were carried out evaluating fifteen typical digital technologies adopted by OSC projects, including building information modelling (BIM), radio frequency identification devices (RFID), global positioning systems (GPS), the Internet of Things (IoT), geographic information systems (GIS), sensors, augmented reality (AR), virtual reality (VR), photogrammetry, laser scanning, artificial intelligence (AI), 3D printing, robotics, big data, and blockchain. This review formulates a clear picture of the current practice of these digital technologies and summarizes the main area of application and limitations of each technology when utilized in OSC. The review also points out their potential and how they can be better adopted to improve OSC practice in the future.

https://www.mdpi.com/2075-5309/10/11/204/pdf

A Systematic Review of Digital Technology Adoption in Off-Site Construction: Current Status and Future Direction towards Industry 4.0

Framework for the coordinated application of two different integrated project delivery platforms

The digitalization of the construction industry (CI) has the aim—among others—to raise the bar of overall productivity. The craft workforce is very relevant on the overall value-chain. Therefore, a boost in this dimension impacts the entire sector. There is a gap in proper methodologies to measure and model productivity. Construction 4.0 novelties provide new approaches for its evaluation and progress. This communication presents a review of workforce productivity assessment and delivers methods focusing primarily on craft workers motion monitoring. Products and services opportunities from Construction 4.0 in the spectrum of craft workforce management include support by embedded sensors for data collection that allow near real-time monitoring. The work developed led to the systematization of a framework to standardize craft workers’ motion productivity. The craft workforce motion productivity framework, Worker 4.0, tenders nine processes integrated on a flowchart to streamline task processes assessment and mechanization level. It also sets up a two-handed/two-legged chart system to model craft workers’ activities and operations. The contributions to the body of knowledge are substantiated on the framework creation with the ability to model and assess craft workforce performance. This approach is meant to serve as base point for different stakeholders focusing on skills, efficiency, mechanization and productivity improvements.

https://www.mdpi.com/2075-5309/10/10/169/pdf

Worker 4.0: The Future of Sensored Construction Sites

Automated Construction worker activity classification has the potential to not only benefit the worker performance in terms of productivity and safety, but also the overall project management and control. The activity-level knowledge and indicators that can be extracted from this process may support project decision making, aiding in project schedule adjustment, resource management, construction site control, among others. Previous works on this topic focused on the collection and classification of worker acceleration data using wearable accelerometers and supervised machine learning algorithms, respectively. However, most of these studies tend to consider small sets of activities performed in an instructed manner, which can lead to higher accuracy results than those expected in a real construction scenario. To this end, this paper builds on the results of these past studies, committing to expand this discussion by covering a larger set of complex Construction activities than the current state-of-the-art, while avoiding the need to instruct test subjects on how and when to perform each activity. As such, a Machine Learning methodology was developed to train and evaluate 13 classifiers using artificial features extracted from raw accelerometer data segments. An experimental study was carried out under the form of a realistic activity-circuit to recognise ten different activities: gearing up; hammering; masonry; painting; roughcasting; sawing; screwing; sitting; standing still; and walking; with most activities being a cluster of simpler tasks (i.e. masonry includes fetching, transporting, and laying bricks). Activities were initially separated and tested in three different activity groups, before assessing all activities together. It was found that a segment length of 6 s, with a 75% overlap, enhanced the classifier performance. Feature selection was carried out to speed the algorithm running time. A nested cross-validation approach was performed for hyperparameter tuning and classifier training and testing. User-dependent and -independent approaches (differing in whether the system must undergo an additional training phase for each new user) were evaluated. Results indicate that accelerometers can be used to create a robust system to recognise large sets of Construction worker activities automatically. The K-Nearest Neighbours and Gradient Boosting algorithms were selected according to their performances, respectively, for the user-dependent and -independent scenarios. In both cases, the classifiers showed balanced accuracies above 84% for their respective approaches and test groups. Results also indicate that a user-dependent approach using task groups provides the highest accuracy.

Activity classification using accelerometers and machine learning for complex construction worker activities

The construction industry faces multiple challenges, where transition to circular production is key. Digitalisation is a strategy to increase the sector’s productivity, competitiveness, and efficiency. However, digitalisation also impacts environmental goals, such as those concerning more eco-friendly solutions, energy efficiency, products recycling, and sustainability certifications. These strategies rely on data, understood as digital, interoperable, incremental and traceable. Data related concepts, such as digital data templates (DDT) and digital building logbooks (DBL), contribute to “good data”. Despite some research focused on each one, little importance has yet been given to their combination. Relevant relationships and overlaps exist, as they partially share the exact same data through the built environment life cycle. This research aims to provide improved understanding on the role of these concepts and their contribution to a more circular industry. The review develops conceptualisations where DDT and DBL are complementary and framed within an incremental digital twin construction (DTC). Misconceptions or confrontations between these three solutions can therefore stand down, for the benefit of a data-driven priority. To increase understanding and reduce misconceptions, our study developed the “Digital data-driven concept” (D3c). This concept contribution is the ability to structure, store, and trace data, opening way to streamlined digital transformation impacting circular built environment concerns.

Incremental Digital Twin Conceptualisations Targeting Data-Driven Circular Construction

https://cyberleninka.org/article/n/1081432.pdf

Pedro Mêda

Papers

Framework for the coordinated application of two different integrated project delivery platforms

Worker 4.0: The Future of Sensored Construction Sites

Activity classification using accelerometers and machine learning for complex construction worker activities

Incremental Digital Twin Conceptualisations Targeting Data-Driven Circular Construction

BIM Methodology, a New Approach - Case Study of Structural Elements Creation☆