The digitalization of the architecture, engineering, and construction (AEC) industry has demonstrated the revolutionary potential of integrating blockchain technology with building information modelling (BIM). However, the selection of the most appropriate blockchain solution is a multiple-criteria decision-making (MCDM) problem, which is usually influenced by conflicting criteria and deep uncertainty. To overcome this, the present study proposes an extended Fermatean fuzzy weighted aggregated sum product assessment (Extended FF-WASPAS) model. Unlike existing Fermatean fuzzy WASPAS (FF-WASPAS) methods, which are based on a single expert and may be biased, the proposed model incorporates the evaluations of multiple decision makers (DMKs) through a consensus-driven mechanism to ensure unbiased and accurate results. A case study is conducted to evaluate five leading blockchain platforms, Hyperledger Fabric, Polkadot, Tezos, Ethereum, and Algorand, under eight BIM-related criteria. The result indicates that Ethereum is the best blockchain platform to digitalize BIM compared to the other platforms because it is scalable, interoperable, secure, and has a wide range of applications in the real world. Sensitivity analysis over a wide range of parameter values, as well as DMKs assigned with different weight sets, confirmed the stability of the ranking. Furthermore, a quantitative comparative analysis with FF multiple criteria group decision-making (FF-MCGDM) and FF-WASPAS approaches, as well as a qualitative analysis with existing models in various fuzzy environments, confirms the robustness and reliability. Overall, the study provides a strong, interpretable, and consensus-based decision-support framework with high practical value for AEC stakeholders who wish to deploy secure, transparent, and efficient blockchain-enabled BIM solutions.
This study presents SmartMix Web3, a framework combining ensemble machine learning and blockchain technology to optimize low-carbon concrete design. It addresses two key challenges: (1) the limitations of conventional models in predicting concrete performance, and (2) ensuring data reliability and overcoming collaboration issues in AI-driven sustainable construction. Validated with 61 real-world experiments in Cameroon and 752 mix designs, the framework shows major improvements in predictive accuracy and decentralized trust. To address the first research question, a stacked ensemble model comprising Extreme Gradient Boosting (XGBoost)âRandom Forest and a Convolutional Neural Network (CNN) was developed, achieving a 22% reduction in Root Mean Square Error (RMSE) for compressive strength prediction and embodied carbon estimation compared to traditional methods. The 29% reduction in Mean Absolute Error (MAE) results confirms the superiority of Extreme Learning Machine (EML) in low-carbon concrete performance prediction. For the second research question, SmartMix Web3 employs blockchain to ensure tamper-proof traceability and promote collaboration. Deployed on Ethereum, it automates verification of tokenized Environmental Product Declarations via smart contracts, reducing disputes and preserving data integrity. Federated learning supports decentralized training across nine batching plants, with Secure Hash Algorithm (SHA)-256 checks ensuring privacy. Field implementation in Cameroon yielded annual cost savings of FCFA 24.3 million and a 99.87 kgCO2/m3 reduction per mix design. By uniting EML precision with blockchain transparency, SmartMix Web3 offers practical and scalable benefits for sustainable construction in developing economies.
Increasing expectancy for efficiency in the delivery of building projects and the adoption of lean production processes for construction has made the necessity for the development of an integrated system for cost estimating, cost monitoring, cost control, and payments in the construction lifecycle important. Existing 5D BIM tools are used to estimate the cost of projects during the preconstruction period. There is a lack of integration between the 5D BIM models, existing progress monitoring tools, and payment systems used in construction. Lack of standardization in the use of model elements through the project lifecycle has also been identified as one of the factors limiting automation in 5D BIM. Construction project monitoring can be automated by combining modern technologies that allow for visualization of building progress (Laser scanners, computer vision) with 5D BIM cost estimation tools. These project monitoring tools can be combined with Artificial Intelligence (AI), and Smart contracts to develop an integrated lifecycle system for cost management in construction. This paper examines existing systems used in 5D BIM to develop integrated practices and systems that will streamline the process of cost estimating, cost monitoring, cost control, and cash flow in the construction supply chain. This will reduce the inefficiency that exists today with traditional contracts and payment applications that do not interact with the 5D BIM application. By leveraging a standardized classification ID system throughout a project life cycle and applying AI and smart contract, features like cost estimation cost control, and payments can be fully streamlined, integrated, and automated. A case study of an existing construction project utilizing 5D BIM was examined. According to the study, 5D BIM is used in the pre-construction stage of a cost estimation project. It was also revealed that 5D BIM improves project cost visualization and budget control.
To rationalize and automate public civil engineering works, it is crucial to directly utilize the information produced by the contractor for quality/ as-built inspection, and progress measurement.In this study, a highly reliable common data environment that utilizes blockchain and smart contracts to ensure tamper resistance and traceability of construction management information on quality and progress was developed and proved through verification tests in two project sites.
Zongliang Zhang, Sherong Zhang, Zhiyong Zhao, Lei Yan ¡ 6 authors
Improving the design efficiency of hydropower hub buildings and promoting the application of intelligence in engineering construction and operation management have been critical issues in hydropower engineering construction. The major challenges are mainly reflected in the difficulties of multidisciplinary collaboration and the inefficient coordination of work involving multiple parties. This paper proposes a building information modeling (BIM)-based technology system architecture for digital design, intelligent construction, and intelligent operation, which combines BIM technology with geographic information system (GIS), computer aided engineering (CAE), internet of things (IoT), artificial intelligence (AI), and other technologies. The proposed system includes a BIM-based multiprofessional forward collaborative design method, a BIM-based engineering construction management model, and a real-time safety analysis and evaluation technology system based on actual measured safety information and construction. The hydroelectrical engineering BIM (HydroBIM) comprehensive control platform is developed. In the planning and design stage, the platform enables the whole process and full-professional collaborative digital design. In the engineering construction management stage, it supports the whole process and all-round information management and control of contract, schedule, quality, safety, and investment. In the operation management stage, the platform facilitates the integrated management of engineering safety evaluation, early warning, and emergency plan based on monitoring data and the consistency criterion of positive and negative evaluation. The application of this technology in more than 20 engineering, has proven to improve the efficiency of design and analysis of hydropower hub buildings and the intelligence level of engineering construction and operation management, and overcome the difficulties of multi-professional collaboration in the design stage and low efficiency of multi-participant coordination in the construction stage.
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Private-ownership of roads in Nigeria is still at the deliberation stage. In other words roads (tarred and untarred) are owned by Federal, State and Local authorities in Nigeria. Most of these roads, however, share a common characteristic of being âunsafe at any speedâ, at any time of the day. This is as a result of the low quality of the road components, structures and patterns. For example, road surfaces are undulating and rough. Also, the poor standard of road infrastructure like guard railings/barriers; pavement marking and signs; illumination levels, traffic signals, horizontal/vertical alignment and sight lines contribute largely to the increasing carnage on Nigerian road network. This trend persists because authorities in Nigeria have practically relegated to the background regular road safety audit operations. This is an inevitable aspect of modern methods of road administration and management, which determines a number of traffic potentials concerning highway high collision locations; protection of errant vehicles from light poles, trees, ditches, replacement of damaged and missing signs, street lighting, capacity and level of service analysis. Finally, this paper suggests commissioning of a National Road Research Fund, with a view to developing an efficient road safety audit operational system. Also, the introduction of private initiatives and a Community-based Approach in road administration, as well as decentralization of road administration framework at all levels, will greatly help âengineer outâ potentially unsafe features across Nigerian roads, towards a better road traffic environment in the 21st century.