Intelligent Infrastructure Research Articles

THE ROLE OF INTELLIGENCE IN NIGERIA'S NATIONAL SECURITY: A CRITICAL ASSESSMENT (2011-2023)

This paper systematically analyses the function of intelligence in Nigeria's national security between 2011 and 2023, with a specific emphasis on countering both established and developing risks including terrorism, insurgency, banditry, cybercrime, and oil theft. The analysis reveals notable deficiencies in inter-agency cooperation, technology integration, and the incorporation of community-based intelligence, which have impeded the efficacy of intelligence operations, despite substantial investments in intelligence infrastructure. Based on Rational Choice Theory, this analysis assesses the methods by which intelligence organizations collect, analyse, and employ intelligence to reduce security threats. The study utilizes a research design that involves a thorough literature analysis to assess the current academic research on intelligence and national security in the Nigerian setting. The results emphasize the necessity for more cooperation among security services, better integration of technology, and further engagement of the community in intelligence operations. This study enhances scholarly discourse and offers practical suggestions for policymakers to bolster Nigeria's intelligence infrastructure in tackling its intricate security issues.

Cybersecurity and defense in intelligent transportation systems

Intelligent Transportation Systems (ITS) have become increasingly crucial for enhancing transportation networks' efficiency, safety, and security. ITS includes advanced technologies such as electronic sensors, data transmission, and intelligent control technologies, providing better driver and rider services. However, these technologies pose significant security and privacy risks, in which attackers can easily disrupt transportation systems. This paper presents a survey of the challenges with ITS, the engineering requirements, attack challenges, and related work of securing ITS. This survey also discussed the elements of ITS, including advanced traffic management systems, connected and automated vehicles, intelligent infrastructure, and integrated data analytics, as well as the role of government policies, public-private partnerships, and stakeholder engagement. The aim is to look at present security challenges associated with ITS and provide better ways to the system. which is critical for creating smart cities and increasing the importance of individual and public transportation systems.

Application of intelligent self-organizing algorithms in UAV cooperative inspection of power distribution networks

In the rapidly evolving technological landscape, the advent of collaborative Unmanned Aerial Vehicle (UAV) inspections represents a revolutionary leap forward in the monitoring and maintenance of power distribution networks. This innovative approach harnesses the synergy of UAVs working together, marking a significant milestone in enhancing the reliability and efficiency of infrastructure management. Despite its promise, current research in this domain frequently grapples with challenges related to efficient coordination, data processing, and adaptive decision-making under complex and dynamic conditions. Intelligent self-organizing algorithms emerge as pivotal in addressing these gaps, offering sophisticated methods to enhance the autonomy, efficiency, and reliability of UAV collaborative inspections. In response to these challenges, we propose the MARL-SOM-GNNs network model, an innovative integration of Multi-Agent Reinforcement Learning, Self-Organizing Maps, and Graph Neural Networks, designed to optimize UAV cooperative behavior, data interpretation, and network analysis. Experimental results demonstrate that our model significantly outperforms existing approaches in terms of inspection accuracy, operational efficiency, and adaptability to environmental changes. The significance of our research lies in its potential to revolutionize the way power distribution networks are inspected and maintained, paving the way for more resilient and intelligent infrastructure systems. By leveraging the capabilities of MARL for dynamic decision-making, SOM for efficient data clustering, and GNNs for intricate network topology understanding, our model not only addresses current shortcomings in UAV collaborative inspection strategies but also sets a new benchmark for future developments in autonomous infrastructure monitoring, highlighting the crucial role of intelligent algorithms in advancing UAV technologies.

Generative AI Applications in Architecture, Engineering, and Construction: Trends, Implications for Practice, Education & Imperatives for Upskilling—A Review

This study investigates the current landscape of generative AI and LLM applications in architecture, engineering, and construction (AEC), focusing on trends, practical implications, educational strategies, and imperatives for upskilling. Employing a six-stage systematic review sourced from Google Scholar, Scopus and Web of Science, 120 papers were analyzed to provide a comprehensive understanding of the role of these technologies in shaping the future of the AEC industry. By addressing these objectives, the research contributes to enhancing knowledge about the potential impacts of generative AI and LLMs on the AEC industry and provides insights into strategies for leveraging these technologies effectively. This study underscores the transformative impact of AI and advanced technologies on the AEC sector and education. By enhancing learning experiences and optimizing construction processes, AI fosters personalized education and efficient project management. The study’s significance lies in its identification of necessary skills and competencies for professionals, ensuring effective AI integration. Implications include the need for continuous professional development, formal education, and practical training to leverage AI’s potential fully. This paves the way for sustainable, intelligent infrastructure and accessible, adaptive learning environments, driving innovation and efficiency in both fields.

Intelligent Infrastructure and Construction: A New Transdisciplinary Journal Focusing on the Use of Information Technologies in Civil Engineering

The field of infrastructure and construction engineering is evolving to address economic, social, and environmental resilience, facing numerous challenges along the way [...]

Damage Detection and Localization Methodology Based on Strain Measurements and Finite Element Analysis: Structural Health Monitoring in the Context of Industry 4.0

This paper investigates the integration of Structural Health Monitoring (SHM) within the frame of Industry 4.0 (I4.0) technologies, highlighting the potential for intelligent infrastructure management through the utilization of big data analytics, machine learning (ML), and the Internet of Things (IoT). This study presents a success case focused on a novel SHM methodology for detecting and locating damages in metallic aircraft structures, employing dimensional reduction techniques such as Principal Component Analysis (PCA). By analyzing strain data collected from a network of sensors and comparing it to a baseline pristine condition, the methodology aims to identify subtle changes in local strain distribution indicative of damage. Through extensive Finite Element Analysis (FEA) simulations and a PCA contribution analysis, the research explores the influence of various factors on damage detection, including sensor placement, noise levels, and damage size and type. The findings demonstrate the effectiveness of the proposed methodology in detecting cracks and holes as small as 2 mm in length, showcasing the potential for early damage identification and targeted interventions in diverse sectors such as aerospace, civil engineering, and manufacturing. Ultimately, this paper underscores the synergistic relationship between SHM and I4.0, paving the way for a future of intelligent, resilient, and sustainable infrastructure.

Research on Vehicle-Road Intelligent Capacity Redistribution and Cost Sharing in the Context of Collaborative Intelligence

The vehicle-road collaborative intelligence approach has become an industry consensus. It can efficiently tackle the technical hurdles and reduce the performance requirements and costs of on-board perception and computing devices. There is a need for in-depth quantitative studies to optimize the allocation of vehicle-road intelligent capabilities for collaborative intelligence. However, current research tends to focus more on qualitative analysis, and there is little research on the redistribution of vehicle and roadside intelligent capabilities. In this paper, we present a model for distributing perception and computing capabilities between vehicle-side and roadside, ensuring to meet the needs of various autonomous driving levels. Meanwhile, the collaborative intelligence approach will also introduce the costs of intelligent infrastructure deployment, energy, and maintenance. Different roads have varying scene characteristics and usage intensities. It is necessary to conduct a cost-effectiveness analysis of the intelligent deployment of different road types. A vehicle-road cost allocation model is developed based on the lifecycle traveled distance of vehicles and the lifecycle traffic flow of various roads to evaluate the function-cost effectiveness. Our study presents several vehicle-road intelligent schemes that meet the needs of various autonomous driving levels and selects Beijing for case analysis. The results indicate that primary intelligent infrastructure can reduce the lifecycle cost of the vehicle-side intelligent scheme for intermediate autonomous driving from ¥65,301 to ¥37,703, and advanced intelligent infrastructure can reduce the lifecycle cost for advanced autonomous driving from ¥126,938 to ¥42,180. Considering the distributed cost of vehicle-side and roadside, urban roads in Beijing have higher function-cost effectiveness compared to highways, especially urban expressways, which are expected to generate 43.3 times the vehicle-function-cost benefits after the advanced intelligent upgrades. The corresponding research findings can serve as a reference for city managers to make decisions on intelligent road deployment.

Deep learning-based voxel sampling for particle therapy treatment planning

Objective. Scanned particle therapy often requires complex treatment plans, robust optimization, as well as treatment adaptation. Plan optimization is especially complicated for heavy ions due to the variable relative biological effectiveness. We present a novel deep-learning model to select a subset of voxels in the planning process thus reducing the planning problem size for improved computational efficiency. Approach. Using only a subset of the voxels in target and organs at risk (OARs) we produced high-quality treatment plans, but heuristic selection strategies require manual input. We designed a deep-learning model based on P-Net to obtain an optimal voxel sampling without relying on patient-specific user input. A cohort of 70 head and neck patients that received carbon ion therapy was used for model training (50), validation (10) and testing (10). For training, a total of 12 500 carbon ion plans were optimized, using a highly efficient artificial intelligence (AI) infrastructure implemented into a research treatment planning platform. A custom loss function increased sampling density in underdosed regions, while aiming to reduce the total number of voxels. Main results. On the test dataset, the number of voxels in the optimization could be reduced by 84.8% (median) at <1% median loss in plan quality. When the model was trained to reduce sampling in the target only while keeping all voxels in OARs, a median reduction up to 71.6% was achieved, with 0.5% loss in the plan quality. The optimization time was reduced by a factor of 7.5 for the total AI selection model and a factor of 3.7 for the model with only target selection. Significance. The novel deep-learning voxel sampling technique achieves a significant reduction in computational time with a negligible loss in the plan quality. The reduction in optimization time can be especially useful for future real-time adaptation strategies.

Intelligent Health Surveillance: Cognitive IoT Enhanced by Rapid Machine Learning

Abstract: The quality of life for diabetic patients is significantly improved through continuous monitoring. Integrating various technologies such as the Internet of Things (IoT), embedded software, communication technologies, artificial intelligence, and smart devices helps reduce the financial burden on the healthcare system. Advances in communication technologies have facilitated personalized and remote healthcare. To meet the growing demand for advanced e-health applications, developing intelligent healthcare systems and increasing the number of applications connected to the network is essential. Consequently, to achieve critical needs like high bandwidth and energy efficiency, the 5G network must support smart healthcare applications. This research proposes an intelligent infrastructure for monitoring diabetes patients using machine learning methods. The architecture incorporates smart devices, sensors, and mobile phones to provide comprehensive coverage of the patient's body. Data collected from the patient is analyzed and classified using machine learning to produce a diagnosis. Several machine learning methods were tested to evaluate the proposed prediction system, and simulation results indicated that the Sequential Minimum Optimization (SMO) method provides higher classification accuracy, sensitivity, and precision than other techniques.

Digital twin-driven intelligent operation and maintenance platform for large-scale hydro-steel structures

Large-scale hydro-steel structures (LS-HSS) are pivotal in hydraulic engineering, boasting high lift, expansive apertures, and discharge capacities. Conventional methods of operation and maintenance for LS-HSS require a digital revolution to ensure safety, reliability, and durability. This study introduces a novel approach, harnessing the power of a digital twin (DT) to integrate physical and virtual elements in LS-HSS operation and maintenance seamlessly. A digital twin modeling framework of LS-HSS operation and maintenance is first presented, incorporating five levels of a physical entity, a virtual entity, DT data, services, and connections. Subsequently, a DT-driven intelligent operation and maintenance (DT-IOM) platform is constructed and applied, which features real-time mapping, closed-loop control, dynamic health assessment, and intelligent decision-making. This platform provides a pathway for digitally transforming conventional LS-HSS operation and maintenance and paves the way for a new era of intelligent infrastructure management. The proposed approach has been successfully implemented and validated with multiple engineering projects, including radial gates of reservoir spillways, plain gates of flood discharge orifices, and emergency gates of pumped storage power stations. The impressive results demonstrate a significant anomaly response and processing efficiency improvement of over 60%. Moreover, the platform has substantially reduced field inspection time and equipment failure rate by approximately 50% and 20%, respectively. These tangible benefits further highlight the platform’s functional advantages, including its capability for IoT connection, modeling and integration, virtual-real interaction, 2D/3D visualization, and service expansion. The results demonstrate the unique features in the closed loop of perception, analysis, decision-making, and optimization for DT-IOM, especially for integrating data, models, knowledge, and services in LS-HSS intelligent operation and maintenance.

Intelligent Infrastructure for Traffic Monitoring Based on Deep Learning and Edge Computing

In the field of traffic management and control systems, we are witnessing a symbiotic evolution, where intelligent infrastructure is progressively collaborating with smart vehicles to produce benefits for traffic monitoring and security, by rapidly identifying hazardous behaviours. This exponential growth is due to the rapid development of deep learning in recent years, as well as the improvements in computer vision models. These technologies allow for monitoring tasks without the need to install numerous sensors or stop the traffic, using the extensive camera network of surveillance cameras already present in worldwide roads. This study proposes a computer vision-based solution that allows for real-time processing of video streams through edge computing devices, eliminating the need for Internet connectivity or dedicated sensors. The proposed system employs deep learning algorithms and vision techniques that perform vehicle detection, classification, tracking, speed estimation, and vehicle geolocation.

Enhancing Urban Living in Smart Cities Using the Internet of Things (IOT)

The widespread applications of smart cities through Internet of Things (IoT) have the potential to cause a revolution in urban living. This study covers the role of IoT technologies in improving urban life: resource management, environment preservation, and public utilities provision. IoT technologies are supported by the seamless integration of physical devices and Internet connectivity that always help save energy, improve transportation, and manage waste. IoT technology in smart cities creates new ideas to enhance urban living. The IoT allows seamless communication between systems and items, enabling quick data collection and analysis. This is a major technological development. This improves resources, automobile traffic, and administration management. Energy efficiency, waste management, and intelligent infrastructure may help cities become more sustainable and environmentally friendly. In that procedure, urban cities can become more sustainable. However, strong legislative frameworks are needed to handle data privacy and computer network security. Smart cities enabled through the IoT can change cities. This shift may be achieved with thorough planning and technological advances. This might create resilient, inclusive urban ecosystems that foster collaboration between communities, corporations, and governments.

Intelligent Infrastructure Facilitating Sequence Recommendation for Cybersecurity Education Systems

The ability to incorporate original and adapted data into query-based storage structures to provide dynamic and timely service to sequence recommendation systems is a continuous goal of learning management systems. This can be a challenging goal when data integrity and student privacy are paramount. We are developing a hybrid machine learning-assisted system (CyberTaliesin) for cybersecurity educational support. In this poster, we present the early building blocks of the system involving the use of federated knowledge graphs as a trusted knowledge source capable of learning from “less restricted” models such as large language models. How can integrating these tools yield a flexible system that improves sequence recommendations, facilitates concepts such as adaptive and personalized learning, and achieves improved competency-based educational outcomes?

Can ESG Integration Enhance the Stability of Disruptive Technology Stock Investments? Evidence from Copula-Based Approaches

This paper provides an investigation into the dependence structure among different disruptive technology sectors driving the Fourth Industrial Revolution and scrutinizes the impact of ESG integration on shaping investments in different tech stock sectors in the presence of ESG consideration, represented by the ESG stock index, versus without specific ESG consideration, represented by the general stock index. The results show that (i) C-vine outperforms R-vine and D-vine when modeling the dependence structure of tech sectors. Intelligent infrastructure is the most crucial sector, with substantial reliance on smart transportation and advanced manufacturing. (ii) ESG integration reduces dependence, especially tail dependence, between tech sectors and the stock market, which benefits the future security sector the most and future communication the least. (iii) ESG integration mitigates risk spillover between tech sectors and the stock market, particularly benefiting final frontiers and intelligent infrastructure. The decrease in downside spillover is more significant compared to upside scenarios. For downside risk, spillover from tech sectors to stock indices is more reduced than the reverse, while the opposite holds for upside risk. These sectoral findings offer insights for market participants in financial market investments, financial regulators in risk management, and listed companies in ESG disclosure.

Synergistic design of mechanical properties between implantable sensor and concrete based on three-dimensional refined numerical model

With the ongoing advancement in the construction and development of intelligent infrastructures, there has been a significant integration of sensors within structures. Portland Cement Concrete (PCC) pavement, being a predominant form of transportation infrastructures, necessitates comprehensive analysis due to its critical role. Given that flexural strength is a paramount design criterion for PCC pavements, concrete beams are selected as the primary focus for this study. Initially, a sophisticated three-dimensional numerical model is constructed. This model intricately incorporates the concepts of concrete damaged plasticity (CDP) and the cohesive zone model (CZM) to examine the interaction between the sensor and the concrete beam. The veracity of the numerical model is rigorously validated through methodical three-point bending tests, ensuring its reliability and accuracy. Subsequently, an innovative method for the synergistic design and assessment of the mechanical properties of the implantable sensor in conjunction with the concrete component is introduced. This methodology is underpinned by a novel synergistic performance index system. The system is stratified into different layers, with synergy degree as the target layer, and bearing performance, deformation coordination, and damage control as the criterion layer. In the final phase of the research, the study delves into the impacts of various factors such as size parameters, packaging materials, and positioning of the sensor on its synergistic performance with the concrete. The insights derived from this research are poised to offer valuable guidance for the integrated synergistic design of sensors. This is envisioned to facilitate optimal integration, ensuring long-term and precise monitoring of concrete structures, thereby contributing significantly to the field of intelligent infrastructure construction and maintenance.

3D Printing of Anisotropic Piezoresistive Pressure Sensors for Directional Force Perception.

Anisotropic pressure sensors are gaining increasing attention for next-generation wearable electronics and intelligent infrastructure owing to their sensitivity in identifying different directional forces. 3D printing technologies have unparalleled advantages in the design of anisotropic pressure sensors with customized 3D structures for realizing tunable anisotropy. 3D printing has demonstrated few successes in utilizing piezoelectric nanocomposites for anisotropic recognition. However, 3D-printed anisotropic piezoresistive pressure sensors (PPSs) remain unexplored despite their convenience in saving the poling process. This study pioneers the development of an aqueous printable ink containing waterborne polyurethane elastomer. An anisotropic PPS featuring tailorable flexibility in macroscopic 3D structures and microscopic pore morphologies is created by adopting direct ink writing 3D printing technology. Consequently, the desired directional force perception is achieved by programming the printing schemes. Notably, the printed PPS demonstrated excellent deformability, with a relative sensitivity of 1.22 (kPa*wt. %)-1 over a substantial pressure range (2.8 to 8.1kPa), approximately fivefold than that of a state-of-the-art carbon-based PPS. This study underscores the versatility of 3D printing in customizing highly sensitive anisotropic pressure sensors for advanced sensing applications that are difficult to achieve using conventional measures.

Unmanned aerial vehicle–human collaboration route planning for intelligent infrastructure inspection

AbstractMotivated by the strengths of unmanned aerial vehicle (UAV), the UAV–human collaboration route planning (UHCRP) for intelligent infrastructure inspection is a problem worthy of discussion to help reduce human costs and minimize the risk of noninspected infrastructures under limited resources. To facilitate UHCRP, this paper proposes a novel deep reinforcement learning (DRL)‐based approach to well handle multi‐source uncertain features and constraints at a fast speed. To begin with, UHCRP is mathematically described and reformulated as a dual interdependent deep reinforcement learning (diDRL) framework to reflect real‐world scenarios. Afterward, a novel policy network named the attention‐based deep neural network (A‐DNN) is introduced to learn the route planning decisions for the combinatorial optimization problem. In particular, A‐DNN is made up of an encoder and a dual decoder for UAV and human inspection, where the multi‐head attention mechanism is incorporated to generate richer representations for model performance improvement. Performance of the proposed dual multi‐head attention model (DAM) has been tested in simulations and a real‐world case study regarding wind farm inspection. Results indicate that DAM under the sampling decoding strategy can deliver a high‐quality path plan and show better generalizability for larger scale problem sizes compared to single‐head attention model (SAM), multi‐head attention model (AM), and two baseline models, namely OR‐Tools and genetic algorithm. Moreover, DAM trained by randomly generated data can be directly employed to solve the practical problem with standardization of inputs. Overall, DRL integrates decision‐making for inspection method selection and inspected infrastructure selection, providing adaptive and intelligent inspection path planning for UAV and human in complex and dynamic engineering environments.

Cost Analysis of Vehicle-Road Cooperative Intelligence Solutions for High-Level Autonomous Driving: A Beijing Case Study

The development of the vehicle-road cooperative intelligence can effectively resolve the current technical impediment and cost quandary associated with high-level autonomous driving. Nevertheless, the intelligent infrastructure entails initial deployment costs and ongoing energy consumption and maintenance costs, necessitating a comprehensive and quantitative analysis of the costs of intelligent infrastructure and the corresponding changes in comprehensive costs. The cost evaluation model for the cooperative intelligent system is designed in this paper, considering the corresponding intelligent infrastructure layout scheme for different road types within the technical framework. The intelligent configuration and corresponding cost transfer from roadside to vehicle side under the synergy effect is also analyzed. Using Beijing as a case study, the results indicate that the deployment of intelligent infrastructure will effectively reduce acquisition and usage costs of high-level intelligent vehicles and achieve a greater “reuse” effect by serving more intelligent connected vehicles (ICVs). Compared to the vehicle intelligence, collaborative intelligence will reduce cumulative total costs by more than ¥200 billion from 2023 to 2050, even with the inclusion of intelligent infrastructure’s costs.

Intelligent Infrastructures Using Deep Learning Based Applications for Energy Optimization

Intelligent Infrastructures Using Deep Learning Based Applications for Energy Optimization

Utilisation of artificial intelligence for the provision of information services at the University of Limpopo libraries

In the wake of the fourth industrial revolution, artificial intelligence has been used in every aspect of human activity, including library operations. Artificial intelligence refers to the process of collaborating with robotic machines to improve job performance, generate increased competence and drive economic progress. In the library and information services, artificial intelligence can carry out a series of operations such as information retrieval. The purpose of this study was to investigate the utilisation of artificial intelligence for the provision of information services at the University of Limpopo library in South Africa. The study used a qualitative content analysis to gather data. The findings indicate that artificial intelligence can be used to provide information services in the library such as responding to students' enquiries and helping users to locate books faster. However, the findings further indicate that the University of Limpopo library must have adequate artificial intelligence infrastructure in place to integrate with the library management system to provide effective information services. The study recommends that an intelligent robotic machine be adopted and applied to the information service at the University of Limpopo library to deal with students' enquiries regarding the availability of the books and the location of the books in the library. The study also recommends that the University of Limpopo library provide training programmes and workshops to help library professionals improve their knowledge of artificial intelligence technologies. The study proposes a framework for the utilisation of artificial intelligence for the provision of information services in the fourth industrial revolution at the University of Limpopo libraries in South Africa.