Template-Type: ReDIF-Article 1.0 Author-Name: Venkata Surendra Kumar Author-X-Name-First: Venkata Surendra Author-X-Name-Last: Kumar Author-Name: Sukhwinder Sharma Author-X-Name-First: Sukhwinder Author-X-Name-Last: Sharma Author-Name: Sudha Kiran Kumar Gatala Author-X-Name-First: Sudha Kiran Kumar Author-X-Name-Last: Gatala Author-Name: Tirupathi Rao Bammidi Author-X-Name-First: Tirupathi Rao Author-X-Name-Last: Bammidi Author-Name: Ravi Kumar Batchu Author-X-Name-First: Ravi Kumar Author-X-Name-Last: Batchu Author-Name: Anil Kumar Vadlamudi Author-X-Name-First: Anil Kumar Author-X-Name-Last: Vadlamudi Title: Navigating the next wave with innovations in distributed ledger frameworks Abstract: The latest study sheds light on distributed ledger technologies (DLTs) outside blockchain systems. The first section of this article introduces DLTs, focusing on blockchain as the main paradigm. It highlights three critical characteristics of blockchain: decentralisation, transparency, and security, and emphasises how blockchain is transforming various industries, including supply chain management and finance. Subsequently, the discussion shifts to new developments and approaches in the DLT space. It introduces next-generation ledgers designed to address traditional blockchains' scalability, energy efficiency, and interoperability challenges. The study delves into modern innovations that achieve higher transaction speeds and greater flexibility, such as hybrid models and directed acyclic graphs (DAGs). A significant portion is dedicated to how these advanced DLTs are used to transform sectors like healthcare government, secure patient data management, and enhance transparency and citizen participation. The article also addresses the challenges and ethical considerations of using these technologies. Finally, the paper predicts that DLTs will improve efficiency and innovation in industries outside blockchain technology. To maximise these new technologies' potential, research and interdisciplinary collaboration are essential. Journal: Int. J. of Critical Infrastructures Pages: 1-24 Issue: 1 Volume: 22 Year: 2026 Keywords: blockchain; decentralisation; cryptocurrency; smart contracts; ledger security; distributed computing; digital identity; interoperability; scalability; tokenisation. File-URL: http://www.inderscience.com/link.php?id=151573 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:22:y:2026:i:1:p:1-24 Template-Type: ReDIF-Article 1.0 Author-Name: Ahmed Moursi Author-X-Name-First: Ahmed Author-X-Name-Last: Moursi Author-Name: Samer El-Zahab Author-X-Name-First: Samer Author-X-Name-Last: El-Zahab Author-Name: Tarek Zayed Author-X-Name-First: Tarek Author-X-Name-Last: Zayed Title: Criticality assessment model for water distribution networks Abstract: The Canadian Infrastructure Report Card of 2016 rates the water system as 'good', but with 29% of pipelines in fair to poor condition, demanding urgent repairs costing $60 billion. Municipalities struggle to prioritise asset rehabilitation due to financial constraints. This study aims to develop a criticality model for water pipeline prediction, integrating expert insights. Three dimensions - economic, environmental/operational, and social - are assessed using the paprika technique. Sensitivity analysis identifies key factors influencing criticality. The model combines criticality and performance indexes to form a priority index, aiding municipalities in strategic capital planning. By pinpointing critical areas requiring immediate attention, this model enhances infrastructure management decision making. Journal: Int. J. of Critical Infrastructures Pages: 59-91 Issue: 1 Volume: 22 Year: 2026 Keywords: assent management; risk management; paprika; criticality index. File-URL: http://www.inderscience.com/link.php?id=151574 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:22:y:2026:i:1:p:59-91 Template-Type: ReDIF-Article 1.0 Author-Name: Deepak Tulsiram Patil Author-X-Name-First: Deepak Tulsiram Author-X-Name-Last: Patil Author-Name: Amiya Bhaumik Author-X-Name-First: Amiya Author-X-Name-Last: Bhaumik Author-Name: Ashutosh Kolte Author-X-Name-First: Ashutosh Author-X-Name-Last: Kolte Title: Optimising inventory management in commercial construction through IoT for enhanced cost efficiency Abstract: The internet of things (IoT) may be integrated into stock management in the industrial creation business to improve task delivery timeliness and cost effectiveness. The study analyses how IoT technology may reduce manual errors, automate inventory monitoring, and give real-time data to improve decision-making. A radical literature review reveals construction inventory management issues include theft, material waste, and inefficient supply networks. We combine qualitative and quantitative studies to focus on managed production IoT device deployment. This observation analyses stock stages before and after IoT generation implementation using 458 samples, showing that inventory management performance and stability have improved. The results demonstrate how the internet of things may transform operational optimisation. Material waste reduction, on-web page productivity, and inventory accuracy improved significantly. We offer an internet of things (IoT)-based inventory management architecture with analytical tables and graphs illustrating performance advantages and fee savings. The speech discusses multinational IoT integration efforts, including operational issues and acceptance challenges. The final paragraph shows how the internet of things can change building stock management. This article also covers future research goals and limits, focusing on IoT technology conversion and production management software growth. Journal: Int. J. of Critical Infrastructures Pages: 92-116 Issue: 1 Volume: 22 Year: 2026 Keywords: internet of things; IoT; inventory management; commercial construction; cost efficiency; real-time tracking; supply chain; automation; and productivity. File-URL: http://www.inderscience.com/link.php?id=151575 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:22:y:2026:i:1:p:92-116 Template-Type: ReDIF-Article 1.0 Author-Name: Sindhu P. Menon Author-X-Name-First: Sindhu P. Author-X-Name-Last: Menon Title: A comprehensive survey on the role of explanation in artificial intelligence: a case study on prediction of gross calorific value of coal Abstract: The study presented here could act as a basis for researchers interested in learning about essential components of the nascent and quickly developing field of research on explainable artificial intelligence (XAI). SHAP-Xgboost is applied to show the working principle of XAI. This is archived by analysing the coal content in the coal reserves. SHapley Additive explanations will be proposed as a revolutionary XAI for this aim. SHAP allows users to understand the extent of relationships between each unique input data along with its corresponding output, as well as rank input variables in order of efficacy. SHAP was combined with extreme gradient boosting (xgboost) (SHAP-Xgboost) which is one of the latest technological developments. SHAPXgboost was able to model GCV accurately (R<SUP align="right"><SMALL>2</SMALL></SUP> = 0.99) using proximate and ultimate analysis (chemical content in coal) from the coal samples. These significant discoveries pave the way for the development of high-interpretability algorithms to learn coal properties and point out crucial variables. Journal: Int. J. of Critical Infrastructures Pages: 25-58 Issue: 1 Volume: 22 Year: 2026 Keywords: explainable artificial intelligence; XAI; artificial intelligence; gross calorific value; explainability. File-URL: http://www.inderscience.com/link.php?id=151576 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:22:y:2026:i:1:p:25-58 Template-Type: ReDIF-Article 1.0 Author-Name: Sombat Trivisvavet Author-X-Name-First: Sombat Author-X-Name-Last: Trivisvavet Author-Name: Winai Wongsurawat Author-X-Name-First: Winai Author-X-Name-Last: Wongsurawat Title: Vertical integration for stakeholder management of hydroelectric power megaproject construction in the Lao People's Democratic Republic (Lao PDR) Abstract: Lao national policy of becoming the 'Battery of Asia' has driven the construction of numerous hydroelectric power projects (HPPs). The purpose of this research is to analyse the critical roles of internal and external stakeholders. Data is gathered through in-depth interviews with internal and external stakeholders of two mega-HPPs. We found that deep collaboration and trust among internal stakeholders are critical for success. Such collaboration and trust can be achieved by not only solid communications and strictly following the contract agreement, but also through strategic choices that can limit excessive transaction costs and foster credible commitments of future benefit sharing among internal stakeholders. The critical requirements for a successful management of external stakeholders are the mitigation of environmental impacts. These factors have a performance-enhancing effect upon mega-HPP construction. The results speak to the following critical infrastructure problem domains: long-term investment, stakeholder engagement, and environmental management in critical infrastructure construction. Journal: Int. J. of Critical Infrastructures Pages: 117-132 Issue: 1 Volume: 22 Year: 2026 Keywords: internal stakeholders; external stakeholders; megaproject; stakeholder management; hydroelectric power project; HPP. File-URL: http://www.inderscience.com/link.php?id=151577 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:22:y:2026:i:1:p:117-132 Template-Type: ReDIF-Article 1.0 Author-Name: Wei Qi Author-X-Name-First: Wei Author-X-Name-Last: Qi Title: An adaptive recognition of abnormal behaviour in deep excavation support construction site of high-rise buildings Abstract: To address the problems of high target false acceptance rates, low accuracy in abnormal behaviour recognition, and lengthy recognition times in traditional methods, this study proposes an adaptive recognition approach for abnormal behaviour in deep excavation support construction sites of high-rise buildings. Key frames are extracted from surveillance videos using the fractional Fourier transform, and object detection is implemented with spatiotemporal graph convolutional network models. Based on the target detection results, a CNN-LSTM model is used to achieve adaptive recognition of abnormal behaviour by capturing the temporal and spatial features of the target. Experimental results show that the proposed method achieves a minimum target false acceptance rate of 2.43%, a maximum recognition accuracy of 99.12%, and a minimum processing time of 0.19 s. Journal: Int. J. of Critical Infrastructures Pages: 1-17 Issue: 7 Volume: 22 Year: 2026 Keywords: high-rise buildings; deep foundation pit support; construction site; abnormal behaviour; adaptive recognition; key frames; CNN-LSTM. File-URL: http://www.inderscience.com/link.php?id=151633 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijcist:v:22:y:2026:i:7:p:1-17 Template-Type: ReDIF-Article 1.0 Author-Name: Xiaozhu Li Author-X-Name-First: Xiaozhu Author-X-Name-Last: Li Author-Name: Jiankang Wu Author-X-Name-First: Jiankang Author-X-Name-Last: Wu Title: BIM parametric modelling analysis of instability of concrete building components under continuous vibration Abstract: To address the challenges in accurately modelling and assessing the stability of concrete building components under continuous vibration, this study presents a novel BIM parametric modelling and analysis method. An instability analysis framework incorporating six correction parameters is established through precise quantification of four key parameters: aspect ratio, moment of inertia, elastic modulus, and constraint conditions. Simultaneously, a critical load calculation model integrating geometric characteristics with material nonlinearity is developed. A dual-parameter instability criterion is proposed, which couples dynamic response with cumulative energy dissipation, enabling a more comprehensive evaluation of component behaviour. Furthermore, a probabilistic calculation model based on five-level instability criteria is established to quantify failure risk. The performance of the proposed method is validated through experimental studies. Results indicate that the method achieves a stable modelling matching degree consistently above 95%, while the accuracy of instability analysis reaches over 96.5%, demonstrating its effectiveness and reliability for engineering applications. Journal: Int. J. of Critical Infrastructures Pages: 16-31 Issue: 8 Volume: 22 Year: 2026 Keywords: continuous vibration; concrete buildings; building instability; BIM parametric modelling. File-URL: http://www.inderscience.com/link.php?id=152009 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijcist:v:22:y:2026:i:8:p:16-31 Template-Type: ReDIF-Article 1.0 Author-Name: Ye He Author-X-Name-First: Ye Author-X-Name-Last: He Author-Name: Bijian Zhou Author-X-Name-First: Bijian Author-X-Name-Last: Zhou Author-Name: Ying Cai Author-X-Name-First: Ying Author-X-Name-Last: Cai Author-Name: Yibin Zhang Author-X-Name-First: Yibin Author-X-Name-Last: Zhang Title: Extraction of crack characteristics and local damage detection in complex hydraulic concrete structures Abstract: Crack detection in complex hydraulic structures is often challenged by background interference, which obscures crack contours and hampers accurate damage localisation. This paper proposes a novel method to address this issue. First, an image compression technique integrating YCbCr conversion, visually weighted wavelet transform, and embedded zero-tree coding is introduced to enhance crack saliency and reduce redundancy. Subsequently, crack edges are continuously and precisely extracted using an adaptive dual-threshold Canny operator. Finally, pixel-level damage detection is achieved with an encoder-decoder model based on SegNet, trained via the Nadam optimiser. Experimental results demonstrate the superiority of our approach, achieving a detection accuracy of 96.2%, a mean intersection over union of 95.6%, and an F1-score of 0.97, significantly outperforming existing methods. The proposed technique provides a reliable and robust solution for the intelligent diagnosis of hydraulic engineering structures. Journal: Int. J. of Critical Infrastructures Pages: 1-15 Issue: 8 Volume: 22 Year: 2026 Keywords: hydraulic construction; concrete structure; crack characteristics; damage detection. File-URL: http://www.inderscience.com/link.php?id=152010 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijcist:v:22:y:2026:i:8:p:1-15 Template-Type: ReDIF-Article 1.0 Author-Name: Zhaoyu Wu Author-X-Name-First: Zhaoyu Author-X-Name-Last: Wu Author-Name: Weirui Cai Author-X-Name-First: Weirui Author-X-Name-Last: Cai Author-Name: Genyuan Zhang Author-X-Name-First: Genyuan Author-X-Name-Last: Zhang Author-Name: Weichen Long Author-X-Name-First: Weichen Author-X-Name-Last: Long Author-Name: Lizhong Peng Author-X-Name-First: Lizhong Author-X-Name-Last: Peng Title: An edge computing-based fast restoration for urban medium- and low-voltage distribution networks Abstract: The rapid and reliable restoration of urban medium- and low-voltage distribution networks is paramount for sustaining economic activities and social well-being. However, conventional centralised restoration methods are increasingly inadequate due to their inherent limitations in handling communication delays, heterogeneous real-time data integration, and the high computational complexity of stochastic optimisation, leading to prolonged outages and reduced service reliability. To address these challenges, this research proposes a fast power recovery method based on distributed edge computing for urban medium- and low-voltage distribution networks. The method enhances restoration efficiency through localised data processing, improved temporal performance, and the integration of heterogeneous data sources. Employing Box-Cox-combined Z-scale conversion for non-Gaussian temporal datasets and principal component-enhanced Dempster-Shafer deduction for information amalgamation, the approach transmutes multi-criteria recovery into singular-goal optimisation via decision matrices. Probabilistic voltage restrictions are reformulated as definitive quadratic mixed-integer constraints through sample average approximation, while second-degree conical relaxation manages non-linear current equations to establish a tractable mixed-integer quadratically constrained programming framework. Experimental outcomes demonstrate 2.89-minute recovery intervals, success probabilities exceeding 95%, and 0.82-0.91 load distribution equilibrium, exhibiting superior performance relative to comparative methodologies. Journal: Int. J. of Critical Infrastructures Pages: 1-21 Issue: 9 Volume: 22 Year: 2026 Keywords: edge computing; medium and low voltage distribution network; rapid restoration of power supply; DS reasoning mechanism. File-URL: http://www.inderscience.com/link.php?id=152365 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijcist:v:22:y:2026:i:9:p:1-21 Template-Type: ReDIF-Article 1.0 Author-Name: Jing Xu Author-X-Name-First: Jing Author-X-Name-Last: Xu Title: Integrating IoT and machine learning for scalable anomaly detection in smart city infrastructure Abstract: People all over the world can connect a lot of smart things to the internet of things (IoT). These tools can talk to other tools in the same family without any help from people. The internet of things (IoT) lets us get and look at a lot of data. Many good things could come from this. A lot of data is made when more things join the IoT. You might find strange things after reading this. It has a lot of different kinds of things. Standard ways to keep an eye on hacking threats need to handle and process different kinds of data in different ways. This might not work well for files that have a lot of different parts. But data from more than one kind of network gadget can hold more kinds of data. It will help you find strange things more quickly. Journal: Int. J. of Critical Infrastructures Pages: 1-16 Issue: 10 Volume: 22 Year: 2026 Keywords: data pre-processing; anomaly detection dataset; benchmarking models; evolution of anomaly detection techniques; internet of things; IoT. File-URL: http://www.inderscience.com/link.php?id=152499 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijcist:v:22:y:2026:i:10:p:1-16