The urbanization problems we face may be alleviated using innovative digital technology. However, employing these technologies entails the risk of creating new urban problems and/or intensifying the old ones instead of alleviating them. Hence, in a world with immense technological opportunities and at the same time enormous urbanization challenges, it is critical to adopt the principles of responsible urban innovation. These principles assure the delivery of the desired urban outcomes and futures. We contribute to the existing responsible urban innovation discourse by focusing on local government artificial intelligence (AI) systems, providing a literature and practice overview, and a conceptual framework. In this perspective paper, we advocate for the need for balancing the costs, benefits, risks and impacts of developing, adopting, deploying and managing local government AI systems in order to achieve responsible urban innovation. The statements made in this perspective paper are based on a thorough review of the literature, research, developments, trends and applications carefully selected and analyzed by an expert team of investigators. This study provides new insights, develops a conceptual framework and identifies prospective research questions by placing local government AI systems under the microscope through the lens of responsible urban innovation. The presented overview and framework, along with the identified issues and research agenda, offer scholars prospective lines of research and development; where the outcomes of these future studies will help urban policymakers, managers and planners to better understand the crucial role played by local government AI systems in ensuring the achievement of responsible outcomes.

Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda

In the healthcare domain, a transformative shift is envisioned towards Healthcare 5.0. It expands the operational boundaries of Healthcare 4.0 and leverages patient-centric digital wellness. Healthcare 5.0 focuses on real-time patient monitoring, ambient control and wellness, and privacy compliance through assisted technologies like artificial intelligence (AI), Internet-of-Things (IoT), big data, and assisted networking channels. However, healthcare operational procedures, verifiability of prediction models, resilience, and lack of ethical and regulatory frameworks are potential hindrances to the realization of Healthcare 5.0. Recently, explainable AI (EXAI) has been a disruptive trend in AI that focuses on the explainability of traditional AI models by leveraging the decision-making of the models and prediction outputs. The explainability factor opens new opportunities to the black-box models and brings confidence in healthcare stakeholders to interpret the machine learning (ML) and deep learning (DL) models. EXAI is focused on improving clinical health practices and brings transparency to the predictive analysis, which is crucial in the healthcare domain. Recent surveys on EXAI in healthcare have not significantly focused on the data analysis and interpretation of models, which lowers its practical deployment opportunities. Owing to the gap, the proposed survey explicitly details the requirements of EXAI in Healthcare 5.0, the operational and data collection process. Based on the review method and presented research questions, systematically, the article unfolds a proposed architecture that presents an EXAI ensemble on the computerized tomography (CT) image classification and segmentation process. A solution taxonomy of EXAI in Healthcare 5.0 is proposed, and operational challenges are presented. A supported case study on electrocardiogram (ECG) monitoring is presented that preserves the privacy of local models via federated learning (FL) and EXAI for metric validation. The case-study is supported through experimental validation. The analysis proves the efficacy of EXAI in health setups that envisions real-life model deployments in a wide range of clinical applications.

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Explainable AI for Healthcare 5.0: Opportunities and Challenges

Recent advancements in sensor technology have resulted in the collection of massive amounts of measured data from the structures that are being monitored. However, these data include inherent measurement errors that often cause the assessment of quantitative damage to be ill-conditioned. Attempts to incorporate a probabilistic method into a model have provided promising solutions to this problem by considering the uncertainties as random variables, mostly modeled with Gaussian probability distribution. However, the success of probabilistic methods is limited due the lack of adequate information required to obtain an unbiased probabilistic distribution of uncertainties. Moreover, the probabilistic surrogate models involve complicated and expensive computations, especially when generating output data. In this study, a non-probabilistic surrogate model based on wavelet weighted least squares support vector machine (WWLS-SVM) is proposed to address the problem of uncertainty in vibration-based damage detection. The input data for WWLS-SVM consists of selected wavelet packet decomposition (WPD) features of the structural response signals, and the output is the Young’s modulus of structural elements. This method calculates the changes in the lower and upper boundaries of Young’s modulus based on an interval analysis method. Considering the uncertainties in the input parameters, the surrogate model is used to predict this interval-bound output. The proposed approach is applied to detect simulated damage in the four-story benchmark structure of the IASC-ASCE SHM group. The results show that the performance of the proposed method is superior to that of the direct finite element model in the uncertainty-based damage detection of structures and requires less computational effort.

Uncertainty Handling in Structural Damage Detection via Non-Probabilistic Meta-Models and Interval Mathematics, a Data-Analytics Approach

Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.

https://www.mdpi.com/1424-8220/21/2/463/pdf

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering.

: Technological growth is changing our everyday living, making it smarter and more convenient day by day; Smart society 5.0, Healthcare 5.0, Agriculture 5.0 are only a few examples indicative of our fast-evolving lifestyle. The Industrial Revolution 5.0 (IR 5.0) encapsulates future industry development trends to achieve prosperity beyond jobs by incorporating more intelligence in our everyday living with the help of cutting-edge technologies such as Explainable Artificial Intelligence. This paper reviews the enabling technologies for Industry 5.0 and suggests some pertinent research areas requiring more focus. The transition of manufacturing processes from mass production to mass personalization, the anticipated reliance on Cyber-Physical Systems (CPS) and digital twins is visualized, to identify the gaps in fully realizing the revolution. The operations of smart factories to enhance the overall productivity, modern workforce comprising of human-machine collaboration, means of heterogeneous data transmission & data interoperability, and security & privacy issues are reviewed to identify hot research spots, that will eventually fill in the gaps within societal domains to realize Industry 5.0. The potential of the new domain of Explainable Artificial intelligence to understand the application of right tools in a data connected Industry 5.0 compliant smart society is explored. Altogether, this research explores several research challenges and opportunities linked with IR 5.0.

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Towards Industrial Revolution 5.0 and Explainable Artificial Intelligence: Challenges and Opportunities

Recent developments in artificial intelligence (AI), in particular machine learning (ML), have been significantly advancing smart city applications. Smart infrastructure, which is an essential component of smart cities, is equipped with wireless sensor networks that autonomously collect, analyze, and communicate structural data, referred to as “smart monitoring”. AI algorithms provide abilities to process large amounts of data and to detect patterns and features that would remain undetected using traditional approaches. Despite these capabilities, the application of AI algorithms to smart monitoring is still limited due to mistrust expressed by engineers towards the generally opaque AI inner processes. To enhance confidence in AI, the “black-box” nature of AI algorithms for smart monitoring needs to be explained to the engineers, resulting in so-called “explainable artificial intelligence” (XAI). However, when aiming at improving the explainability of AI algorithms through XAI for smart monitoring, the variety of AI algorithms requires proper categorization. Therefore, this review paper first identifies objectives of smart monitoring, serving as a basis to categorize AI algorithms or, more precisely, ML algorithms for smart monitoring. ML algorithms for smart monitoring are then reviewed and categorized. As a result, an overview of ML algorithms used for smart monitoring is presented, providing an overview of categories of ML algorithms for smart monitoring that may be modified to achieve explainable artificial intelligence in civil engineering.

Artificial Intelligence Techniques for Smart City Applications

Extrusion-based additive manufacturing (AM), or three-dimensional (3D) printing, has matured into a set of advanced methods to automate the construction of large-scale concrete structures, while minimizing cost and material waste. However, current AM data models are inadequate for 3D concrete printing due to insufficient incorporation of information on the relationships between process, material, and geometry, which may cause redundancy, information loss, and inconsistencies. Aiming at improving AM data modeling for concrete printing, this paper proposes a metamodeling approach for AM of concrete structures, referred to as “printing information modeling”, which takes advantage of building information modeling (BIM). As will be shown in this paper, the BIM-based printing information model, serving as a metamodel, incorporates the digital data triplet of process, material, and geometry parameters to generate computer numerical control (CNC) commands that may readily be used for concrete printing. A validation test is performed, which instantiates the printing information model, using a BIM model, for generating CNC commands, enabling optimal digital data exchange from BIM models to concrete printers. As a result of this study, it is demonstrated that printing information modeling adequately defines the information required for AM of concrete structures using a BIM-based approach, showing promising potential to improve current AM data modeling efforts.

BIM-Based Concrete Printing

With the rapid development of modern technologies, autonomous or robotic construction sites are becoming a new reality in civil engineering. Despite various potential benefits of the automation of construction sites, there is still a lack of understanding of their complex nature combining physical and cyber components in one system. A typical approach to describing complex system structures is to use tools of abstract mathematics, which provide a high level of abstraction, allowing a formal description of the entire system while omitting non-essential details. Therefore, in this paper, autonomous construction is formalised using categorical ontology logs enhanced by abstract definitions of individual components of an autonomous construction system. In this context, followed by a brief introduction to category theory and ologs, exemplary algebraic definitions are given as a basis for the olog-based conceptual modelling of autonomous construction systems. As a result, any automated construction system can be described without providing exhausting detailed definitions of the system components. Existing ologs can be extended, contracted or revised to fit the given system or situation. To illustrate the descriptive capacity of ologs, a lattice of representations is presented. The main advantage of using the conceptual modelling approach presented in this paper is that any given real-world or engineering problem could be modelled with a mathematically sound background.

https://www.mdpi.com/2673-4117/4/1/48/pdf?version=1677668146

Formalising Autonomous Construction Sites with the Help of Abstract Mathematics

In recent years, structural health monitoring (SHM) applications have significantly been enhanced, driven by advancements in artificial intelligence (AI) and machine learning (ML), a subcategory of AI. Although ML algorithms allow detecting patterns and features in sensor data that would otherwise remain undetected, the generally opaque inner processes and black-box character of ML algorithms are limiting the application of ML to SHM. Incomprehensible decision-making processes often result in doubts and mistrust in ML algorithms, expressed by engineers and stakeholders. In an attempt to increase trust in ML algorithms, explainable artificial intelligence (XAI) aims to provide explanations of decisions made by black-box ML algorithms. However, there is a lack of XAI approaches that meet all requirements of SHM applications. This chapter provides a review of ML and XAI approaches relevant to SHM and proposes a conceptual XAI framework pertinent to SHM applications. First, ML algorithms relevant to SHM are categorized. Next, XAI approaches, such as transparent models and model-specific explanations, are presented and categorized to identify XAI approaches appropriate for being implemented in SHM applications. Finally, based on the categorization of ML algorithms and the presentation of XAI approaches, the conceptual XAI framework is introduced. It is expected that the proposed conceptual XAI framework will provide a basis for improving ML acceptance and transparency and therefore increase trust in ML algorithms implemented in SHM applications.

Daniel Luckey

Papers

Artificial Intelligence Techniques for Smart City Applications

BIM-Based Concrete Printing

Formalising Autonomous Construction Sites with the Help of Abstract Mathematics

Explainable Artificial Intelligence to Advance Structural Health Monitoring