Dillip Kumar Das

The reuse of industrial by-products in geotechnical engineering requires systematic optimization frameworks that balance multiple performance criteria simultaneously. Traditional mix design approaches for cement-stabilized pond ash rely primarily on heuristic evaluation of individual strength parameters, often neglecting integrated, data-driven decision support. This study proposes an artificial intelligence–assisted multi-criteria decision framework to identify the optimal cement content for pond ash stabilization. Experimental investigations were conducted to evaluate maximum dry density (MDD), optimum moisture content (OMC), and unconfined compressive strength (UCS) at 7, 14, and 28 days for six pond ash–cement mixtures. The resulting dataset was integrated into an objective weighting and ranking architecture that combines the Entropy method for automated weight derivation with the Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) method for alternative prioritization. The Entropy algorithm identified early-age UCS as the most influential criterion, reflecting its high discriminative capacity within the dataset. The MARCOS-based ranking revealed that the 85:15 pond ash–cement proportion provides the optimal compromise, achieving balanced early- and long-term strength performance with acceptable compaction behavior. The proposed framework serves as a transparent, reproducible decision-support model for intelligent selection of geomaterials and demonstrates how artificial intelligence–assisted optimization can enhance sustainable geotechnical material design. The methodology is extensible to other industrial-waste stabilization systems, thereby supporting data-driven infrastructure engineering.

1. Engineering education is essential for the sustainable socio-economic development, preparing graduates to deal with various problems around the globe. Since engineers are involved in solving soc...

Although smart city strategies increasingly employ emerging technologies to improve urban services, existing research often treats these technologies in isolation from broader socio-technical systems. Similarly, urban resilience scholarship conceptualises cities as complex adaptive systems but rarely integrates digital infrastructure into formal resilience frameworks. This study examined how digital technologies can be holistically integrated into urban systems to support sustainable, adaptive, and resilient smart cities. It also developed a systems-thinking–based framework to guide the governance and design of inclusive and environmentally responsive urban systems. The study was conducted using a qualitative multi-method approach combining literature review and comparative case analysis of digitally advanced cities, including Singapore, Amsterdam, Wuhan, Copenhagen, Barcelona, and cities in Karnataka, India. The findings indicate that resilience depends less on the scale of technological deployment and more on the integration of digital infrastructure with adaptive governance, interoperability, and citizen participation. Drawing on systems thinking and system dynamics, the study proposes a digital feedback framework in which reinforcing loops enhance sensing, decision intelligence, inclusivity, and sustainability, while balancing loops that regulate risks related to privacy, inequality, technological lock-in, and environmental rebound effects. The study contributes a systems-based framework linking smart city development with adaptive urban resilience.

The transition to automated vehicles (AVs) introduces complex human factors and system-level challenges across Society of Automotive Engineers (SAE) Levels 0–5, with profound implications for the long-term viability of future transport infrastructure. Drawing on a synthesis of socio-technical, cognitive, and behavioural adaptation theories, this study develops an integrated framework to analyse the evolving relationships among driving automation, human behaviour, system risks, and urban sustainability. The findings demonstrate that human-factor risks are inherently nonlinear, meaning they do not decrease proportionally as technology advances; instead, risk profiles peak significantly at intermediate automation levels (SAE 2–3) due to supervisory fatigue and delayed takeovers, introducing severe traffic flow volatility and localised micro-congestion that directly compromise the environmental efficiency of sustainable transport systems. As these risks reconfigure into institutional and digital infrastructure dependencies at higher levels (SAE 4–5), the primary constraint shifts toward network readiness. Through an analysis of real-world AV deployment case studies and a structured narrative literature review, this paper identifies critical operational discontinuities and mixed-traffic complexities that threaten urban grid resilience. This study proposes a conceptual framework that translates these cross-level socio-technical insights into actionable deployment pathways, providing policymakers with adaptive governance models, transportation planners with mixed-traffic management strategies aimed at preserving network efficiency, infrastructure agencies with physical and digital readiness criteria for long-term asset sustainability, and AV developers with human–machine interface optimisation frameworks to secure human-centric safety within sustainable smart city networks.

Sustainable transport systems are increasingly operating in contexts where uncertainty is no longer the exception but a structural condition

Knowledge workers choose cities based on economic opportunities, infrastructure, quality of life, and housing. While housing remains an attraction, and the influence of its attributes and strategies for developing housing according to the preferences of knowledge workers in major cities is studied, the mid-sized cities in the Global South remain underexplored. This study examined housing attributes and strategies that attract knowledge workers in these cities. Stakeholder and Delphi surveys were used for data collection, and inferential statistical analyses, including Principal Component Analysis, were used for data analysis. Findings revealed that beyond affordability, the availability of a well-balanced mix of types of houses, security and urban amenities is critical for knowledge workers. Grounded in place theory and urban systems analysis, the study emphasised sustainable housing strategies to attract the skilled professionals.

Smart cities utilise advanced technology to enhance the quality of life, economic efficiency, and environmental sustainability of citizens. This transformation is both vital and complex in Africa due to rapid urbanisation and socio-economic challenges. This paper examines the prospects, challenges, and pathways toward smart city development in African cities. The study was conducted through a systematic literature review and case study analyses of initiatives for smart city development in Africa. The findings indicate that infrastructure deficits, financial constraints, weak policy frameworks, limited expertise, and socio-economic inequalities are the key challenges. The high use of mobile technologies, innovation hubs, and increasing policy support have created opportunities. Strategic actions for transforming African cities include strengthening infrastructure through public–private partnerships, developing financial mechanisms, creating coherent policies, promoting inclusivity, and building technical capacity. Technologies such as Information and Communication Technology (ICT) and Artificial Intelligence (AI) are among the key enablers, supporting the growth of Small and Medium-Sized Enterprises (SMEs), improving infrastructure, fostering inclusive governance, managing resources sustainably, and enhancing public services such as healthcare and education. The study also proposes a conceptual framework for smart cities in Africa and outlines a pathway to unlock the continent’s potential for smart cities. It is argued that African cities need to address systemic challenges, leverage unique opportunities, and ensure inclusivity at the urban level. An integrated approach that utilises advanced technologies and prioritises sustainability and resilience is essential for developing smart and inclusive cities.

This study identifies the client and machinery-related factors and examines their interrelationships in construction procurement, employing a systems-thinking approach. Data from stakeholders in the KwaZulu-Natal construction industry were analysed through statistical methods and Applied Systems Analysis (ASA), with causal loop diagrams (CLDs) illustrating how procurement performance evolves via reinforcing and balancing feedback loops. Findings indicate that clients’ emphasis on cost and timely delivery strongly drives procurement decisions but can weaken decision-making, reduce communication, and increase project variations. Machinery-related challenges, including poor maintenance, high hiring costs, and limited access, further exacerbate inefficiencies. When considered together, balancing loops emerge, where strong governance, effective communication, preventive maintenance, and operator training stabilise and improve outcomes. The study contributes theoretically by advancing a systems-based perspective that integrates client governance and machinery management, and practically by identifying leverage points for policymakers, clients, and contractors, such as improving governance, embedding maintenance practices, aligning priorities with resource planning, and institutionalising feedback. Procurement effectiveness is thus shaped by interdependent governance, decision-making, resource management, and systemic learning. Keywords: Applied Systems Analysis; Client; Construction; Machinery, Procurement; Governance