The payment issues in construction projects present major challenges for successful completion of projects. In this proceeding a framework is presented for achieving trust-free automated progress payment of construction projects to eliminate or reduce the payment issues. The framework integrates blockchain, smart contracts, Building Information Modeling (BIM), robotic reality capture solutions, and decentralized cloud technologies for accomplishing automated progress payments without any involvement of the project participants. Achieving a trust-free automated progress payment system would enable secure, efficient, timely and transparent payment of construction projects and would also eliminate payment related trust issues among the project participants.
This research explores LLM-based multi-agent systems for construction management challenges. A framework is proposed, using specialized LLM agents coordinated centrally to automate and optimize project aspects. It integrates diverse memory, APIs, and environmental awareness. Leveraging LLM reasoning and natural language, it incorporates construction knowledge via databases and knowledge graphs. The system demonstrates potential for improved efficiency in scheduling, resource allocation, safety, and decision support. Data quality, computation, and explainability are acknowledged limitations. This work provides a theoretical basis for LLM multi-agent applications in construction.
Digital twins offer solutions to efficiency and decision-making problems of the construction industry by expanding the capabilities of current technologies. However, their acceptance and adoption have not been thoroughly examined, creating barriers to their implementation. This study proposes a conceptual framework in the context of leveraging Technology Acceptance Model (TAM) for digital twins in construction through a systematic review of existing literature. The framework includes a modified TAM enhanced by constructs from the Innovation Diffusion Theory (IDT) to tailor TAM for digital twin technology. The framework also outlines the model constructs, scale item and target audience selection procedures.
YOLOv11, a CNN-based object detection and instance segmentation algorithm, is used to automatically capture Masonry Quality Index (MQI) parameters for existing masonry structures. Training is performed using a suitable dataset for detecting bricks, and its hyperparameters are adjusted systematically for optimal accuracy. A workflow is proposed in which models are trained on the "MCrack1300" dataset and evaluated using orthomosaics of an unreinforced masonry building. Optimal hyperparameters are determined iteratively, and their impact on minimizing loss is compared. The proposed model captures block size distributions and staggering ratios associated with the construction quality of masonry walls.
The architecture, engineering, and construction supply chain is predominantly composed of small and medium enterprises (SMEs). Despite the growing adoption of digital tools such as Building Information Modeling (BIM), SMEs often face challenges streamlining these tools with existing enterprise resource planning (ERP) systems. This paper presents a study that worked with an SME to develop a "customized data environment", inspired by common data environment approaches, linking their BIM and ERP workflows. The paper describes the action research cycle used to develop the prototype. Insights into both the tool and the process evolution will help other SMEs facing similar integration challenges.
Sustainability awareness stoked the need to trace green/sustainable materials throughout the building life cycle to minimize environmental impacts, optimize resource use, and meet the standards of environmentally responsible construction practices. Traditional material tracing systems harness centralized servers, posing security and transparency issues. Blockchain technologies enable alternatives for achieving decentralized systems, which can resolve the limitations of centralized ones. Few blockchain and smart contract frameworks are presented for supply chain management, yet scarce research focused on tracing green/sustainable materials. This study aims to present a decentralized green/sustainable material tracing system framework for building life cycle with blockchain and smart contract technologies.
Crack detection in concrete bridge elements is critical to the bridge's durability and safety. The ability to link cracks with the typy of damaged element, location, and the moment of occurrence is critical for understanding the structure's behaviour. This paper discusses a solution for segmenting structural elements on images and segmenting cracks using a deep learning network trained on a prepared dataset of pre-failure concrete cracks. While the detection of cracks in concrete representing the failure condition is currently a relatively straightforward task, the identification of narrow cracks representing the pre-failure state has not yet received a satisfactory solution.
Increased accessibility to additive manufacturing technology facilitates democratization of manufacturing, bringing it to habitable environments. The operation of additive manufacturing can be hazardous to human health mid-long term. Virtual sensing extends the capabilities of hardware sensors enabling affordable monitoring to ensure safe operation in democratized manufacturing environments. However, the development process has not yet been standardized for informally trained personnel to facilitate the adoption of virtual sensors. This paper presents a case study analysis to propose a standardized process for the data collection and development of virtual sensors for indoor air quality monitoring in democratized manufacturing environments.
The urgent global need to reduce carbon emissions has intensified the focus on transitioning to sustainable energy solutions. Residential heating is a significant contributor to greenhouse gas emissions. As such, understanding the factors influencing this shift towards more sustainable heating solutions is critical. This study employs an Agent-based Model to examine the dynamics of adopting sustainable residential heating systems, with a special emphasis on the transition to a gas-free community. The model integrates individual household preferences, financial capabilities, environmental concerns, and social influences to understand the collective transition. The paper illustrated the methodology through a numerical simulation.
