Structural health monitoring data has not been fully leveraged to support asset management due to a lack of effective integration with other data sets. A building information modeling (BIM) approach is presented to leverage structural monitoring data in a dynamic manner. The approach allows for the automatic generation of parametric BIM models of structural monitoring systems that include time-series sensor data, and it enables data-driven and dynamic visualization in an interactive 3D environment. The approach supports dynamic visualization of key structural performance parameters, allows for the seamless updating and long-term management of data, and facilitates data exchange by generating models compliant with industry foundation classes (IFC). A newly constructed bridge near Stafford, United Kingdom, with an integrated fiber-optic sensor–based monitoring system was used to test the capabilities of the developed approach. The case study demonstrated how the developed approach facilitates more intuitive data interpretation; provides a user-friendly interface to communicate with various stakeholders; allows for the identification of malfunctioning sensors, thus contributing to the assessment of monitoring system durability; and forms the basis for a powerful data-driven asset management tool. In addition, this project highlights the potential benefits of investing in the development of data-driven and dynamic BIM environments.

Purpose: In a circular economy, the goal is to keep materials values in the economy for as long as possible. For the construction industry to support the goal of the circular economy, there is the need for materials reuse. However, there is little or no information about the amount and quality of reusable materials obtainable when buildings are deconstructed. The purpose of this paper, therefore, is to develop a reusability analytics tool for assessing end-of-life status of building materials. Design/methodology/approach: A review of the extant literature was carried out to identify the best approach to modelling end-of-life reusability assessment tool. The reliability analysis principle and materials properties were used to develop the predictive mathematical model for assessing building materials performance. The model was tested using the case study of a building design and materials take-off quantities as specified in the bill of quantity of the building design. Findings: The results of analytics show that the quality of the building materials varies with the building component. For example, from the case study, at the 80th year of the building, the qualities of the obtainable concrete from the building are 0.9865, 0.9835, 0.9728 and 0.9799, respectively, from the foundation, first floor, frame and stair components of the building. Originality/value: As a contribution to the concept of circular economy in the built environment, the tool provides a foundation for estimating the quality of obtainable building materials at the end-of-life based on the life expectancy of the building materials.

Purpose: The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of health and safety risks, which are usually sparse and noisy. Design/methodology/approach: The study focuses on using the big data frameworks for designing a robust architecture for handling and analysing (exploratory and predictive analytics) accidents in power infrastructure. The designed architecture is based on a well coherent health risk analytics lifecycle. A prototype of the architecture interfaced various technology artefacts was implemented in the Java language to predict the likelihoods of health hazards occurrence. A preliminary evaluation of the proposed architecture was carried out with a subset of an objective data, obtained from a leading UK power infrastructure company offering a broad range of power infrastructure services. Findings: The proposed architecture was able to identify relevant variables and improve preliminary prediction accuracies and explanatory capacities. It has also enabled conclusions to be drawn regarding the causes of health risks. The results represent a significant improvement in terms of managing information on construction accidents, particularly in power infrastructure domain. Originality/value: This study carries out a comprehensive literature review to advance the health and safety risk management in construction. It also highlights the inability of the conventional technologies in handling unstructured and incomplete data set for real-time analytics processing. The study proposes a technique in big data technology for finding complex patterns and establishing the statistical cohesion of hidden patterns for optimal future decision making.

The understanding of occupancy patterns has been identified as a key contributor to achieve improvements in energy efficiency in buildings since occupancy information can benefit different systems, such as HVAC (Heating, Ventilation, and Air Conditioners), lighting, security, and emergency. This has meant that in the past decade, researchers have focused on improving the precision of occupancy estimation in enclosed spaces. Although several works have been done, one of the less addressed issues, regarding occupancy research, has been the availability of data for contrasting experimental results. Therefore, the main contributions of this work are: (1) the generation of two robust datasets gathered in enclosed spaces (a fitness gym and a living room) labeled with occupancy levels, and (2) the evaluation of three Machine Learning algorithms using different temporal resolutions. The results show that the prediction of 3–4 occupancy levels using the temperature, humidity, and pressure values provides an accuracy of at least 97%.

In this article, two methods to develop and optimize accompanying building spatial and structural designs are compared. The first, a coevolutionary method, applies deterministic procedures, inspired by realistic design processes, to cyclically add a suitable structural design to the input of a spatial design, evaluate and improve the structural design via the finite element method and topology optimization, adjust the spatial design according to the improved structural design, and modify the spatial design such that the initial spatial requirements are fulfilled. The second method uses a genetic algorithm that works on a population of accompanying building spatial and structural designs, using the finite element method for evaluation. If specific performance indicators and spatial requirements are used (i.e., total strain energy, spatial volume, and number of spaces), both methods provide optimized building designs; however, the coevolutionary method yields even better designs in a faster and more direct manner, whereas the genetic algorithm based method provides more design variants. Both methods show that collaborative design, for example, via design modification in one domain (here spatial) to optimize the design in another domain (here structural) can be as effective as monodisciplinary optimization; however, it may need adjustments to avoid the designs becoming progressively unrealistic. Designers are informed of the merits and disadvantages of design process simulation and design instance exploration, whereas scientists learn from a first fully operational and automated method for design process simulation, which is verified with a genetic algorithm and subject to future improvements and extensions in the community.