Abolghassem Zabihollah

Prototyping has evolved from a simple representational artifact into a central mechanism for learning, communication, risk reduction, and decision-making in engineering design. Despite its widespread adoption across engineering disciplines, existing research remains fragmented across domains, methodologies, and application contexts, making it difficult to distinguish broadly applicable principles from context-specific practices. This paper presents a critical review of prototyping research in engineering design, synthesizing evidence from peer-reviewed journal articles and conference papers from foundational studies of the 1980s to recent developments in rapid prototyping, additive manufacturing, digital engineering, and Industry 4.0 systems. A thematic literature review was conducted to identify recurring principles, domain-dependent variations, emerging trends, and persistent limitations in current prototyping practices. The review examines key factors influencing prototyping effectiveness, including purpose, fidelity, timing, stakeholder involvement, modeling and analysis, risk management, economic considerations, and learning-oriented iteration. Particular attention is given to how uncertainty influences prototyping decisions and the ways in which different uncertainty conditions influence the selection, scope, and implementation of prototyping activities. The findings indicate that prototyping is best understood as a context-dependent decision-support activity whose effectiveness depends on the uncertainties, constraints, stakeholders, and design objectives associated with a specific engineering problem. Although several common principles emerge across engineering domains, substantial differences exist in how prototypes are used to support design decisions and system validation. The review identifies research gaps related to uncertainty-driven fidelity selection, integration of modeling, experimentation, and verification activities, and the limited availability of systematic guidance for selecting prototyping strategies across diverse engineering contexts. Future research should focus on generalized prototyping frameworks, quantitative decision-support methods for uncertainty management, enhanced stakeholder integration, and the continued convergence of physical and virtual prototyping environments in next-generation engineering systems.

Prototyping plays a central role in engineering design by supporting creativity, modeling, experimentation, and validation under conditions of uncertainty. However, existing prototyping approaches remain fragmented across disciplines, with limited integration between design activities, fidelity selection, and decision-making processes. This lack of coherence makes it difficult to systematically guide prototyping across different stages of the engineering design lifecycle. This paper proposes a generalized, context-sensitive prototyping framework developed through a structured and critical synthesis of engineering design literature. The framework integrates four iterative stages: creativity and concept generation, modeling and analysis, prototyping strategy and execution, and experimental validation. It further incorporates key dimensions influencing prototyping decisions, including fidelity levels, risk considerations, stakeholder involvement, and uncertainty management. The framework is derived through thematic analysis of peer-reviewed literature spanning foundational prototyping models and recent developments in digital engineering, rapid prototyping, and AI-supported design. Representative case examples are used to illustrate the applicability of the framework across different engineering contexts. The contribution of this work is a unified conceptual and process-oriented framework that clarifies relationships among prototyping activities and provides a structured perspective for understanding prototyping under uncertainty. This study also identifies limitations of current approaches and outlines directions for future research in adaptive and data-driven prototyping systems.

Falls are a leading cause of bone fractures among the elderly, particularly hip fractures resulting from side falls. This research deals with the feasibility of application of shear-thickening fluids (STFs) to design self-protective wearable devices to rapidly respond to sudden impact due to falls. The device consists of a lightweight, flexible foam structure embedded with STF-filled compartments, which remain soft during normal movements but stiffen upon sudden impact, effectively dissipating energy and reducing force trans-mission to the bones. First, a foam-based sandwich panel filled with STF is fabricated and subjected to several falling scenarios through a ball drop test. The induced strain of the device with and without STF is measured using Fiber Bragg Grating (FBG) sensors. Then, the effect of localized STF is explored by fabricating a soft 3D-printed (TPU) sandwich panel filled with STF at selected cavities. It was observed that the application of STF reduces the induced strain by approximately 50% for the TPU skin device and 30% for the foam-based device. This adaptive response mechanism offers a balance between comfort and protection, ensuring wearability for daily use while significantly lowering fracture risks. The proposed solution aims to enhance fall-related injury prevention for the elderly, improving their quality of life and reducing healthcare burdens associated with fall-related fractures.

As global infrastructure systems face increasing environmental, social, and operational challenges, enhancing their resilience through digital and intelligent technologies has become a strategic priority. Digital Twin (DT) and Artificial Intelligence (AI) technologies offer transformative capabilities for monitoring, predicting, and optimizing infrastructure performance under stress. However, research on their integration within resilience frameworks remains fragmented. This study presents a comprehensive bibliometric analysis to clarify how DT and AI are being applied to strengthen infrastructure resilience (IR). Using data exclusively from the Web of Science (WoS) database, co-occurrence and overlay visualizations were employed to map thematic structures, identify research clusters, and track emerging trends. The analysis revealed six interconnected research domains linking DT, AI, and resilience, including artificial intelligence and industrial applications, digital twins and machine learning, cyber–physical systems, smart cities and sustainability, data-driven resilience modeling, and methodological frameworks. Overlay mapping revealed a temporal shift from early work on sensors and cyber–physical systems toward integrated, sustainability-oriented applications, including predictive maintenance, urban digital twins, and environmental resilience. The findings underscore the need for adaptive and interoperable DT ecosystems incorporating AI-driven analytics, ethical data governance, and sustainability metrics, providing a unified foundation for advancing resilient and intelligent infrastructure systems.

This study investigates the effectiveness of embedding fiber Bragg grating (FBG) sensors in power transmission towers to assess the remaining service life of the structures following impacts from strong winds and hurricanes. FBG sensors monitor the structural integrity of the tower using online measurement of strain variations at critical structural points. The novelty of this work lies in employing a compact long short-term memory (LSTM) framework to estimate the remaining useful lifetime (RUL) from real-time FBG sensor data under both stable and fluctuating wind conditions. To estimate RUL of the tower, LSTM neural network has been implemented, providing predictive insights for proactive maintenance and risk mitigation. A prototype transmission tower was built and experimentally evaluated in a wind tunnel to assess the effectiveness and performance of the proposed RUL model. To simulate different hurricane categories, the experiment was conducted across wind speeds between 0 and 150 mph. FBG sensors installed at critical locations continuously captured real-time strain data, which was transmitted via a low-power micro FBG interrogator to a computer for input into the RUL prediction model. The proposed three-layer LSTM converges rapidly, reducing training and validation loss by nearly two orders of magnitude within 40 epochs, and achieves robust RUL predictions with an average bias of about 50 s on the test set. To quantify structural health, a mathematical health indicator was formulated based on the observed strain responses. The FBG sensors demonstrated high effectiveness in accurately detecting strain variations and monitoring the tower's dynamic behavior under extreme wind loads. The findings support the implementation of condition-based maintenance strategies, enhance safety assessments, and enable early failure detection. This approach not only improves operational reliability but also facilitates timely intervention and maintenance during critical events. Received: 26 June 2025 | Revised: 12 September 2025 | Accepted: 11 November 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Yu Shi: Methodology, software, formal analysis, data curation, writing – original draft, writing – review & editing, visualization, supervision, and project administration. Abolghassem Zabihollah: Conceptualization, methodology, software, validation, investigation, resources, data curation, writing – original draft, writing – review & editing, visualization, supervision, and project administration. Yao-Chi Yu: Methodology, software, formal analysis, data curation, and writing – original draft. Arunima Pathak: Investigation, writing – original draft, and visualization. Oluwaseyi Oyetunji: Investigation.

Foot drop, a form of paralysis affecting ankle and foot control, impairs walking and increases the risk of falls. Effective rehabilitation requires monitoring gait to guide personalized interventions. This study presents a proof-of-concept smart foot–ankle brace integrating low-cost sensors, including gyroscopes, accelerometers, and a Fiber Bragg Grating (FBG) array, with an Arduino-based processing platform. The system captures, in real time, the key locomotion parameters, namely, angular rotation, acceleration, and sole deformation. Experiments using a 3D-printed insole demonstrated that the device detects foot-drop-related gait deviations, with toe acceleration approximately twice that of normal walking. It also precisely detects foot deformation through FBG sensing. These results demonstrate the feasibility of the proposed system for monitoring gait abnormalities. Unlike commercial gait analysis devices, this work focuses on proof-of-concept development, providing a foundation for future improvements, including wireless integration, AI-based gait classification, and mobile application support for home-based or tele-rehabilitation applications.

The growing vulnerability of residential buildings to seismic activity and dynamic loading underscores the need for robust, real-time Structural Health Monitoring (SHM) systems. This study investigates the feasibility of utilizing piezoelectric sensors integrated with advanced signal processing techniques, including Power Spectral Density (PSD) and Short-Time Fourier Transform (STFT), for vibration-based SHM in residential structures. A scaled three-story building prototype was fabricated and subjected to controlled base excitations at 25 Hz and 0.6 g acceleration to evaluate the response under three structural conditions: healthy, randomly damaged, and thickness-damaged. The experimental results revealed that structural degradation significantly altered sensor outputs, with random damage causing irregular signal dispersion and thickness damage introducing additional frequency harmonics. PSD analysis effectively identified shifts in energy distribution, signifying structural degradation, while STFT provided a detailed time-frequency representation, facilitating real-time damage detection. The findings confirm that piezoelectric sensors, when combined with PSD and STFT, can serve as a low-cost, scalable solution for early damage detection in residential buildings, offering a practical framework for enhancing structural resilience against earthquakes and other dynamic forces.

Dynamic environmental loads, such as winds and waves, make the stability of offshore structures at high risk, requiring reliable yet efficient control elements to ensure the stability of such structures under lateral loads. Among the variety of control elements that have been developed to enhance the stability of a structure, shape memory alloy (SMA)-based control elements are promising as they are low-cost, easy to embed into the main control element, and do not need an external power supply. However, cyclic loads may highly influence the performance and functionality of SMA-based elements. The present work investigates the effects of pre-straining SMA components in energy dissipation capacity and its force-displacement behaviour under cyclic loads. The results of experimental tests show that pre-straining SMA by 1.7% significantly reduces the residual deformation and increases the energy dissipation capacity of structures under the short and long-term cyclic loads.

Laminated composite structures are being used in many applications, including aerospace, automobiles, and civil engineering applications, due to their high stiffness to weight ratio. However, composite structures suffer from low ductility and sufficient flexibility to resist against dynamic, particularly impact loadings. Recently, a new generation of laminated composite structures has been developed in which some layers have been filled fully or partially with magnetorheological (MR) fluids; hereafter we call them MR-laminated structures. The present article investigates the effects of MR fluid layers on vibration characteristics and specifically on impact loadings of the laminated composite beams. Experimental works have been conducted to study the dynamic performance of the MR-laminated beams.