Challenges and Opportunities for Automating Physical Compliance on Construction Sites

The construction project lifecycle includes several compliance requirements that need to be checked at multiple levels and at different phases of the project. Inability to comply with these regulations due to lack of time and resources or human oversight can affect the project throughout its service lifecycle with the potential for severe outcomes. Following a number of high-profile failings and owing to the high stakes nature of compliance, digitalisation has been introduced in this field of construction over the past few decades to reduce mistakes and neglect. Although the compliance checking process in the design phase has seen significant digital advancement with artificial intelligence, machine learning and natural language processing, the physical compliance checking process on construction sites still remains largely manual. This paper will present academic research on the industry challenges faced in automating site compliance checking process based on literature studies done in the past. The study highlights the need to address the different challenges and barriers of physical compliance from a more structured construct. The opportunities for process improvement, behavioural change, and technological intervention to improve or in some cases replace manual oversight were also explored. A thematic analysis was performed on the qualitative data of barriers to chronicle the list of challenges that need to be addressed. Findings from this study will help highlight the pressure points faced while conducting compliance checks at sites. This research aims to reduce the knowledge gap between the ailment of checking compliance on construction sites and the tools that can help fix the issue

Site percolation in distorted square and simple cubic lattices with flexible number of neighbors

This paper exhibits a Monte Carlo study on site percolation using the Newmann-Ziff algorithm in distorted square and simple cubic lattices where each site is allowed to be directly linked with any other site if the euclidean separation between the pair is at most a certain distance d, called the connection threshold. Distorted lattices are formed from regular lattices by a random but controlled dislocation of the sites with the help of a parameter {\alpha}, called the distortion parameter. The distinctive feature of this study is the relaxation of the restriction of forming bonds with only the nearest neighbors. Owing to this flexibility and the intricate interplay between the two parameters {\alpha} and d, the site percolation threshold may either increase or decrease with distortion. The dependence of the percolation threshold on the average degree of a site has been explored to show that the obtained results are consistent with those on percolation in regular lattices with extended neighborhood and continuum percolation.Comment: 9 pages, 6 figures, Accepted in Phys. Rev.

The Infrastructure Metaverse Already Exists

The construction industry is currently undergoing a profound digital transformation, primarily driven by the innovative concept of digital twins. The United Kingdom\u27s National Digital Twin Programme (NDTp) stands as a visionary initiative striving to establish a cohesive digital ecosystem wherein all forms of infrastructure, both existing and newly constructed, are replicated in the digital realm. Digital twins are not merely static visual representations; they serve as the cornerstone for a comprehensive information management framework that enables real-time data sharing. This data-sharing capability spans the entire lifecycle of construction projects. What makes this technology particularly powerful is its capacity to facilitate advanced simulations, which are instrumental in assessing the implications of new constructions and even modelling potential natural disasters. The simulations can have a substantial impact on improving infrastructure performance, productivity, transparency, and resource efficiency. The A-EYE Control Tower project aligns its vision with the Digital Twin innovation, both striving to advance the construction industry into a new era characterised by data-driven decision-making. Their joint mission is to elevate construction productivity and foster transparency among stakeholders

Construction Semantics and Generative Pre-trained Transformer (GPT) Language Models

The article explores the impact of ChatGPT, a powerful language model developed by OpenAI, on the construction industry. ChatGPT, with its ability to provide contextually accurate responses to a wide range of queries, has the potential to enhance safety, scheduling, and knowledge dissemination in construction. While it shows promise in tasks like hazard recognition and project scheduling, the article emphasizes the need for cautious optimism and human oversight. The construction industry could benefit from ChatGPT\u27s digital assistance to boost productivity and address skills shortages, but it is essential to recognize the value of human expertise alongside AI advancements

Synergising BIM and Real-Time Data for Improved Efficiency: An Irish Case Study

The evolution of 3D visualisation and the Internet of Things (IoT) presents a substantial opportunity for integrating real-time data with Building Information Models (BIM) to improve its functionality and construction workflow efficiency. Integrating real-time data with BIM can enhance the digital representation of construction buildings’ physical and functional characteristics and provide recordable status of on-site operations. Nevertheless, the integration between Visualisation and IoT technologies with BIM remains in its preliminary stages and faces a myriad of technical and operational challenges. Furthermore, developing advanced solutions to facilitate this complex integration requires a considerable understanding of the viability and feasibility of merging BIM and real-time data sources. This paper presents the early development of a nationally-funded Irish case study deploying a combined camera and tracking solutions that enable the integration of BIM and real-time data through passive data capture. It aims to explore the potential benefits, challenges, and perspectives of integrating the disruptive A-EYE solution for real-time data BIM in Irish construction projects. Semi-structured interviews were conducted with the BIM specialists of an Irish construction company to investigate the data-related challenges to As-built BIM updating. The qualitative data were subjected to thematic analysis to explore their predispositions, expectations, demands, and motivations for utilising real-time data in updating BIM. The research results demonstrated a favourable perspective regarding integrating real-time data sources with BIM, enhancing the efficiency and quality of the As�built BIM development process

The Regional Coupled Suite (RCS-IND1): application of a flexible regional coupled modelling framework to the Indian region at kilometre scale

A new regional coupled modelling framework is introduced – the Regional Coupled Suite (RCS). This provides a flexible research capability with which to study the interactions between atmosphere, land, ocean, and wave processes resolved at kilometre scale, and the effect of environmental feedbacks on the evolution and impacts of multi-hazard weather events. A configuration of the RCS focussed on the Indian region, termed RCS-IND1, is introduced. RCS-IND1 includes a regional configuration of the Unified Model (UM) atmosphere, directly coupled to the JULES land surface model, on a grid with horizontal spacing of 4.4 km, enabling convection to be explicitly simulated. These are coupled through OASIS3-MCT libraries to 2.2 km grid NEMO ocean and WAVEWATCH III wave model configurations. To examine a potential approach to reduce computation cost and simplify ocean initialization, the RCS includes an alternative approach to couple the atmosphere to a lower resolution Multi-Column K-Profile Parameterization (KPP) for the ocean. Through development of a flexible modelling framework, a variety of fully and partially coupled experiments can be defined, along with traceable uncoupled simulations and options to use external input forcing in place of missing coupled components. This offers a wide scope to researchers designing sensitivity and case study assessments. Case study results are presented and assessed to demonstrate the application of RCS-IND1 to simulate two tropical cyclone cases which developed in the Bay of Bengal, namely Titli in October 2018 and Fani in April 2019. Results show realistic cyclone simulations, and that coupling can improve the cyclone track and produces more realistic intensification than uncoupled simulations for Titli but prevents sufficient intensification for Fani. Atmosphere-only UM regional simulations omit the influence of frictional heating on the boundary layer to prevent cyclone over-intensification. However, it is shown that this term can improve coupled simulations, enabling a more rigorous treatment of the near-surface energy budget to be represented. For these cases, a 1D mixed layer scheme shows similar first-order SST cooling and feedback on the cyclones to a 3D ocean. Nevertheless, the 3D ocean generally shows stronger localized cooling than the 1D ocean. Coupling with the waves has limited feedback on the atmosphere for these cases. Priorities for future model development are discussed

Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions

Wetland methane (CH4) emissions (FCH4) are important in global carbon budgets and climate change assessments. Currently, FCH4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent FCH4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that FCH4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between FCH4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between FCH4 and temperature, suggesting larger FCH4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments. Wetland methane emissions contribute to global warming, and are oversimplified in climate models. Here the authors use eddy covariance measurements from 48 global sites to demonstrate seasonal hysteresis in methane-temperature relationships and suggest the importance of microbial processes.Peer reviewe