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Sangeen Khan

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Open access Aug 2026

Numerical Assessment of the Seismic Performance of a Lead-Rubber Base-Isolated Low-Rise RC Frame under Site-Specific Earthquake Excitations

Quetta lies in one of Pakistan's most seismically active regions, where the Building Code of Pakistan (BCP-2007) assigns a design ground acceleration of 0.32 g and UBC-97 Seismic Zone 4 is adopted as the design basis. Despite this hazard, location-specific research on seismic isolation for the local building stock remains scarce. This study evaluates lead-rubber bearing (LRB) base isolation for a four-storey reinforced concrete (RC) special moment-resisting frame representative of local construction. Two identical three-dimensional ETABS models - one fixed-base, one LRB-isolated - were developed in accordance with UBC-97 and ACI 318. Two ground-motion records (Northridge-Reseda, 1994 and Tabas, Iran, 1978) were spectrum-matched to the UBC-97 Zone-4 design spectrum in SeismoMatch and applied in time-history analysis, with material nonlinearity confined to the bearings. Isolation lengthened the fundamental period from 0.799 s to 2.028 s (a ratio of 2.54) and reduced superstructure demand markedly: base shear by 49-70%, roof displacement by 74-86%, inter-storey drift by 90-95% and peak roof acceleration by 42-54%. The corresponding deformation was transferred to the isolation layer, where the bearing displacement reached 330-360 mm - the value governing the required seismic gap. LRB isolation is therefore a technically effective means of reducing seismic demand in low-rise RC frames in Quetta. These findings are conditioned on two ground-motion records, a linear-elastic superstructure and unidirectional excitation; larger record suites, inelastic superstructure modelling and bidirectional excitation are recommended before design-level application

Sangeen Khan, Sami Ullah, Muhammad Habib et al. · 0 citations
Open access Aug 2026

Computer Aided Cognitive BIM Framework for Intelligent Decision Support in Civil Engineering Infrastructure Using Hybrid Artificial Intelligence

The integration of Building Information Modelling (BIM) with artificial intelligence (AI) represents a transformative paradigm in civil engineering infrastructure management. This paper proposes a novel Computer Aided Cognitive BIM (CAC-BIM) framework that leverages hybrid artificial intelligence techniques to provide intelligent decision support for civil engineering infrastructure projects. The framework integrates deep learning, fuzzy logic, knowledge-based systems, and multi-agent architectures within a cognitive computing environment to enhance decision-making processes across the infrastructure lifecycle. The proposed methodology employs a mixed-methods research design combining computational modelling, case study validation, and expert evaluation. Results demonstrate that the CAC-BIM framework achieves a 34.7% improvement in decision accuracy, 28.3% reduction in project delays, and 22.1% cost optimization compared to conventional BIM-assisted approaches. The framework's hybrid AI architecture demonstrates superior performance in handling uncertainty, multi-criteria optimization, and real-time adaptive reasoning in complex infrastructure scenarios. This research contributes to the advancement of intelligent construction management and provides a scalable computational framework for next-generation civil engineering decision support systems.

Waleed Arshad, Hafiza Sarah Iqbal, M. Khan et al. · 0 citations

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