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Generative Pre-Trained Transformer (GPT) language models such as ChatGPT have the potential to revolutionize the construction industry by automating repetitive and time-consuming tasks. This paper presents a study in which ChatGPT was used to generate a construction schedule for a simple construction project. The output from ChatGPT was evaluated by a pool of participants that provided feedback regarding their overall interaction experience and the quality of the output. The results show that ChatGPT can generate a coherent schedule that follows a logical approach to fulfill the requirements of the scope indicated. The participants had an overall positive interaction experience and indicated the potential of such a tool in automating many preliminary and time-consuming tasks. However, the technology still has limitations, and further development is needed before it can be widely adopted in the industry. Overall, this study highlights the advantages of using large language models and Natural Language Processing (NLP) techniques in the construction industry and the need for further research.

Mobile scanning systems are being used more and more frequently in industry, construction, and artificial intelligent applications. More particularly, autonomous scanning plays an essential role in the field of the automatic creation of 3D models of building. This paper presents a critical review of current autonomous scanning systems, discussing essential aspects that determine the efficiency and applicability of a scanning system in real environments. Some important issues, such as data redundancy, occlusion, initial assumptions, the complexity of the scanned scene, and autonomy, are analysed in the first part of the document, while the second part discusses other important aspects, such as pre-processing, time requirements, evaluation, and opening detection. A set of representative autonomous systems is then chosen for comparison, and the aforementioned characteristics are shown together in several illustrative tables. Principal gaps, limitations, and future developments are presented in the last section. The paper provides the reader with a general view of the world of autonomous scanning and emphasizes the difficulties and challenges that new autonomous platforms should tackle in the future.

In this article, a traversing door methodology for building scanning mobile platforms is proposed. The problem of passing through open/closed doors entails several actions that can be implemented by processing 3D information provided by dense 3D laser scanners. Our robotized platform, denominated as MoPAD (Mobile Platform for Autonomous Digitization), has been designed to collect dense 3D data and generate basic architectural models of the interiors of buildings. Moreover, the system identifies the doors of the room, recognises their respective states (open, closed or semi-closed) and completes the aforementioned 3D model, which is later integrated into the robot global planning system. This document is mainly focused on describing how the robot navigates towards the exit door and passes to a contiguous room. The steps of approaching, door-handle recognition/positioning and handle–robot arm interaction (in the case of a closed door) are shown in detail. This approach has been tested using our MoPAD platform on the floors of buildings composed of several rooms in the case of open doors. For closed doors, the solution has been formulated, modeled and successfully tested in the Gazebo robot simulation tool by using a 4DOF robot arm on board MoPAD. The excellent results yielded in both cases lead us to believe that our solution could be implemented/adapted to other platforms and robot arms.

The deployment of autonomous robots in construction remains constrained by the complexity and variability of real-world environments. Conventional programming and single-agent approaches lack the adaptability required for dynamic multi-robot operating conditions, underscoring the need for cooperative, learning-based systems. This paper presents an ROS-based modular framework that integrates Multi-Agent Reinforcement Learning (MARL) into a generic 2D simulation and execution pipeline for cooperative mobile robots in construction-oriented digital environments to enable adaptive task allocation and coordinated execution without predefined datasets or manual scheduling. The framework adopts a centralized-training, decentralized-execution (CTDE) scheme based on Multi-Agent Proximal Policy Optimization (MAPPO) and decomposes the system into interchangeable modules for environment modelling, task representation, robot interfaces, and learning, allowing different layouts, task sets, and robot models to be instantiated without redesigning the core architecture. Validation through an ROS-based 2D simulation and real-world experiments using TurtleBot3 robots demonstrated effective task scheduling, adaptive navigation, and cooperative behavior under uncertainty. In simulation, the learned MAPPO policy is benchmarked against non-learning baselines for multi-robot task allocation, and in real-robot experiments, the same policy is evaluated to quantify and discuss the performance gap between simulated and physical execution. Rather than presenting a complete construction-site deployment, this first study focuses on proposing and validating a reusable MARL–ROS framework and digital testbed for multi-robot task allocation in construction-oriented digital environments. The results show that the framework supports effective cooperative task scheduling, adaptive navigation, and logic-consistent behavior, while highlighting practical issues that arise in sim-to-real transfer. Overall, the framework provides a reusable digital foundation and benchmark for studying adaptive and cooperative multi-robot systems in construction-related planning and management contexts.

The energy monitoring of heritage buildings has, to date, been governed by methodologies and standards that have been defined in terms of sensors that record scalar magnitudes and that are placed in specific positions in the scene, thus recording only some of the values sampled in that space. In this paper, however, we present an alternative to the aforementioned technologies in the form of new sensors based on 3D computer vision that are able to record dense thermal information in a three-dimensional space. These thermal computer vision-based technologies (3D-TCV) entail a revision and updating of the current building energy monitoring methodologies. This paper provides a detailed definition of the most significant aspects of this new extended methodology and presents a case study showing the potential of 3D-TCV techniques and how they may complement current techniques. The results obtained lead us to believe that 3D computer vision can provide the field of building monitoring with a decisive boost, particularly in the case of heritage buildings.

Ensuring that formwork systems are properly installed is essential for construction safety and quality. They have to comply with specific design requirements and meet strict tolerances regarding the installation of the different members. The current method of quality control during installation mostly relies on manual measuring tools and inspections heavily reliant on the human factor, which could lead to inconsistencies and inaccurate results. This study proposes a way to automate the inspection process and presents a framework within which to measure the spacing of the different members of the formwork system using 3D point cloud data. 3D point cloud data are preprocessed, processed, and analyzed with various techniques, including filtering, downsampling, transforming, fitting, and clustering. The novelty is not only in the integration of the different techniques used but also in the detection and measurement of key members in the formwork system with limited human intervention. The proposed framework was tested on a real construction site. Five cases were investigated to compare the proposed approach to the manual and traditional one. The results indicate that this approach is a promising solution and could potentially be an effective alternative to manual inspections for quality control during the installation of formwork systems.

Realistic point cloud models are frequently required in order to create efficient 3D data processing algorithms. In a building information modelling context, for example, the segmentation and object recognition of point clouds become extremely difficult tasks when the inputs of the algorithms are not sufficiently good. This paper proposes a method with which to efficiently solve two of the most important issues during the creation of realistic 3D data with laser scanners: the treatment of specularity in coloured point clouds and the view merging process. We particularly deal with wide-range laser scanners with the objective of generating realistic orthoimages of the main structural elements of the insides of buildings (i.e. walls, ceilings and floors). The new algorithm gathers the coloured points belonging to structural elements and delimits the specular regions originating from non-controlled or directional lighting sources (e.g. flashes). The restoration of these specular regions is solved in a subsequent stage in which several partial views of a structural element are integrated into a unique orthoimage. Finally, the quality of the resulting orthoimage is evaluated by comparing it with the corresponding ground truth image. This method has been tested on a public database, yielding encouraging results.

With an increasing focus on sustainability, human health, and productivity, there is a growing demand for efficient methods to monitor and manage indoor environmental conditions. However, existing monitoring platforms often face challenges related to high costs and limited scalability. This study presents a practical approach to developing a Digital Twin specifically designed for assessing indoor environmental quality (IEQ). A university campus office space served as the proof of concept, illustrating the implementation of the proposed Digital Twin workflow. The platform demonstrated stability with less than 3% data loss. The IEQ dashboard, along with thermal comfort visualization for various clothing types in the 3D environment, highlights the platform’s effectiveness in monitoring IEQ and its potential to enhance indoor experiences. The proposed Digital Twin framework contributes to the growing body of knowledge by offering a scalable and cost-effective solution for indoor environmental monitoring. This study seeks to advance understanding of indoor environments and support data-driven decision-making to drive improvements.

This paper presents an automated workflow for weekly construction progress reporting, streamlining data integration and analysis. The proposed approach focuses on three key areas: planned activities, weekly performance, and projected progress. Inputs include an updated baseline schedule and weekly inspection data, such as images. Outputs, generated autonomously, provide project status and performance metrics, including Earned Value and Planned Value. Leveraging multimodal large language models (MLLMS), the system processes text and images, enabling seamless data integration. Key contributions include a simplified, reliable reporting process that reflects actual construction execution and planning while reducing time and resource demands. The paper also addresses implementation challenges, Al-driven solutions, and scalability for broader construction reporting applications.

The construction industry is witnessing a transformative shift with the integration of advanced technologies, especially in the topic of 3D segmentation. This study underscores the current state and challenges of 3D segmentation, with special emphasis on construction research, and provides an insightful understanding of the latest research developments and trends. The study also looks at the performance metrics of the most relevant techniques, as well as the main limitations and research gaps, highlighting the need for further research in highly-performing techniques based on Deep Learning for point cloud segmentation in construction applications.