Abstract

A class of hybrid control system which combines a passive nonlinear base isolator with an active controller is analyzed. The passive component of the system has a hysteretic behaviour. A single control force is applied on the structural base by means of a new adaptive
control strategy, not requiring knowledge of the system parameters and the excitation and able to handle the typical nonlinearities associated with base isolators. A numerical study is performed to assess the improvement of the behaviour of buildings equipped with this control system.

Abstract

Abstract

The consensus control problem to be discussed in this paper is concerned with nonlinear multi-agent systems with unknown dynamics, unknown fractions, time-varying delays, and input saturation limits. In order to address these difficulties, an adaptive control system is established, incorporating a recurrent general type-2 fuzzy neural network (RGT2FNN) with a biogeography-based optimization (BBO) algorithm. The RGT2FNN is used to model nonlinear functions that are not known offline, and the BBO algorithm also optimizes the parameters of the fuzzy network and performs offline identification of the fractional order by minimizing multi-step model prediction error. In order to make the model more resistant to modeling uncertainties, time-varying delays, and actuator saturation effects, a LMI-based compensator is proposed to ensure the stability of the closed-loop. Lyapunov analysis guarantees the boundedness of consensus errors. The simulation findings prove that the suggested methodology can attain an accurate consensus tracking and strong performance when the uncertainties are harsh, the delays may change with time, and the limits of input saturation are taken into account.OPEN ACCESS Received: 06/01/2026 Accepted: 16/03/2026

Abstract

Ultra-high-performance concrete (UHPC) offers superior mechanical performance but remains limited by high cement content, sustainability concerns, and the lack of reliable constitutive models capable of capturing its full stress–strain response including pre-peak and post-peak behaviour. This study presents an integrated experimental and constitutive modelling investigation of UHPC incorporating ultra-fine waste glass powder, micro silica, fly ash, and different fibre reinforcement. Two UHPC mix designs with a water-to-cementitious material ratio of 0.20 were developed, yielding 24 independent mixtures. Cement was partially replaced with 20% ultra-fine waste glass powder and combined with either micro silica or fly ash. Fibre reinforcement was introduced independently using 2% straight steel fibres and 1.5% crimped polypropylene fibres to evaluate flowability, viscosity, uniaxial compressive strength, modulus of elasticity, splitting tensile strength, flexural strength and drying shrinkage behaviour. Experimental results demonstrate that UHPC with micro silica, UFWGP and steel fibres, prepared following the ACI 239 R-18 mix design, achieved the highest 28 days compressive, splitting tensile and flexural strength of 144.8, 12.8 and 36.1 MPa, respectively. Steel fibre-reinforced mixes exhibited up to a 42% increase in compressive strength and significantly enhanced post-peak ductility compared to the control mix (without fibre). Crimped Polypropylene fibre-reinforced UHPC attained compressive strength up to 129.7 MPa, provided improved strain capacity, and effectively reduced drying shrinkage, highlighting their suitability for crack control and deformation mitigation. The incorporation of UFWGP consistently improved flowability, reduced viscosity, and enhanced mechanical performance, while fly ash improved rheological behaviour but resulted in lower earlyage strength compared to micro silica-based mixes. To capture the compressive response of UHPC, an elasto-damage model previously proposed by Khan and Zahra was reformulated by recalibrating the compression damage parameter (β) using experimentally derived and literature based compressive strength and elastic modulus. The proposed model reproduces both pre-peak and post-peak stress–strain behaviour, with prediction errors generally within ±7% of experimental results ranging from 72.4 to 148.5 MPa. The findings provide robust experimental evidence and a validated constitutive framework for the sustainable design and structural application of UHPC

Abstract

The consensus control problem to be discussed in this paper is concerned with nonlinear multi-agent systems with unknown dynamics, unknown fractions, time-varying delays, and input saturation limits. In order to address these difficulties, an adaptive control system is established, incorporating a recurrent general type-2 fuzzy neural network (RGT2FNN) with a biogeography-based optimization (BBO) algorithm. The RGT2FNN is used to model nonlinear functions that are not known offline, and the BBO algorithm also optimizes the parameters of the fuzzy network and performs offline identification of the fractional order by minimizing multi-step model prediction error. In order to make the model more resistant to modeling uncertainties, time-varying delays, and actuator saturation effects, a LMI-based compensator is proposed to ensure the stability of the closed-loop. Lyapunov analysis guarantees the boundedness of consensus errors. The simulation findings prove that the suggested methodology can attain an accurate consensus tracking and strong performance when the uncertainties are harsh, the delays may change with time, and the limits of input saturation are taken into account.OPEN ACCESS Received: 06/01/2026 Accepted: 16/03/2026