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In this study, a railway superstructure is modeled with a new approach called locally continuous supporting, and its behavior under the effect of moving load is analyzed by using analytical and numerical techniques. The purpose of the study is to demonstrate the success of the new modeling technique.

In the railway superstructure, the support zones are not modeled with discrete spring-damping elements. Instead of this, it is considered to be a continuous viscoelastic structure in the local areas. To model this approach, the governing partial differential equations are derived by Hamilton’s principle and spatially discretized by the Galerkin’s method, and the time integration of the resulting ordinary differential equation system is carried out by the Newmark–Beta method.

Both the proposed model and the solution technique are verified against conventional one-dimensional and three-dimensional finite element models for a specific case, and a very good agreement between the results is observed. The effects of geometric, structural, and loading parameters such as rail-pad length, rail-pad stiffness, rail-pad damping ratio, the gap between rail pads and vehicle speed on the dynamic response of railway superstructure are investigated in detail.

There are mainly two approaches to the modeling of rail pads. The first approach considers them as a single spring-damper connected in parallel located at the centroid of the rail pad. The second one divides the rail pad into several parts, with each of part represented by an equivalent spring-damper system. To obtain realistic results with minimum CPU time for the dynamic response of railway superstructure, the rail pads are modeled as continuous linearly viscoelastic local supports. The mechanical model of viscoelastic material is considered as a spring and damper connected in parallel.

Studies showed that a proactive delivery system employing innovative artificial intelligence (AI) in urban services might perform better. This has become an important national policy for many countries. Thus, this study aims to explore the influence of AI in urban services in Malaysia.

Official documents such as Structure Plan and Government Transformation Programme Policy Document covering various levels of cities in Malaysia, articles related to urban studies mostly written by researchers regarding urban growth in Malaysia and the Urban Development Bulletin from the Federal Department of Town and Country Planning from 1957 to date were reviewed and analysed.

The findings show that the influence of AI in urban services has long existed and been carefully planned by local authorities since colonial times. The development of global digital technology influences the upgrading of AI in urban services in Malaysia. Also, the success of AI in these municipal services is influenced by the rate of information technology literacy among the urban population. These developments have led to the definition of a conceptual city.

This paper's findings and conclusion were based on reviewed literature but did not compromise the strength of this paper. Thus, as part of the implications for future research, mixed-methods research design has been suggested.

As part of the implications, this article intends to promote AI in urban services in Malaysia and other developing countries with similar urban services challenges.

This paper encourages AI applications in urban services because it enhances service delivery performance. This intends to key stakeholders to promote AI via policies across Malaysia's urban services as part of the study implications.