Image-based optical vibration measurement is an attractive alternative to the conventional measurement of structural dynamics predominantly relying on accelerometry. Although various optical vibration monitoring systems are now readily available, their performance is currently not well defined, especially in the context of experimental modal analysis. To this end, this study provides some of the first evidence of the capability of optical vibration monitoring systems in modal identification using input–output measurements. A comparative study is conducted on a scaled model of a 3D building frame set in a laboratory environment. The dynamic response of the model to an impulse excitation from an instrumented hammer, and an initial displacement, is measured by means of five optical motion capture systems. These include commercial and open-source systems based on laser Doppler velocimetry, fiducial markers and marker-less pattern recognition. The performance of these systems is analysed against the data obtained with a set of high-precision accelerometers. It is shown that the modal parameters identified from each system are not always equivalent, and that each system has limitations inherent to its design. Informed by these findings, a guidance for the deployment of the considered optical motion capture systems is given, aiding in their choice and implementation for structural vibration monitoring.

Despite significant advances in the development of high-resolution digital cameras in the last couple of decades, their potential remains largely unexplored in the context of input-output modal identification. However, these remote sensors could greatly improve the efficacy of experimental dynamic characterisation of civil engineering structures. To this end, this study provides early evidence of the applicability of camera-based vibration monitoring systems in classical experimental modal analysis using an electromechanical shaker. A pseudo-random and sine chirp excitation is applied to a scaled model of a cable-stayed bridge at varying levels of intensity. The performance of vibration monitoring systems, consisting of a consumer-grade digital camera and two image processing algorithms, is analysed relative to that of a system based on accelerometry. A full set of modal parameters is considered in this process, including modal frequency, damping, mass and mode shapes. It is shown that the camera-based vibration monitoring systems can provide high accuracy results, although their effective application requires consideration of a number of issues related to the sensitivity, nature of the excitation force, and signal and image processing. Based on these findings, suggestions for best practice are provided to aid in the implementation of camera-based vibration monitoring systems in experimental modal analysis.

The assessment of dynamic performance of large-scale bridges typically relies on the deployment of wired instrumentation systems requiring direct contact with the tested structures. This can obstruct their operation and create unnecessary risks to the involved personnel and equipment. These problems can be readily avoided by using non-contact instrumentation systems. However, the cost of off-the-shelf commercial products often prevents their wide adoption in engineering practice. To this end, the dynamic performance of the biggest one-pylon cable-stayed bridge in Poland is investigated based on data from a consumer-grade digital camera and open access image-processing algorithms. The quality of these data is benchmarked against data obtained from conventional wired accelerometers and a high-end commercial optical motion capture system. Operational modal analysis is conducted to extract modal damping, which has a potential to serve as an indicator of structural health. The dynamic properties of the bridge are evaluated against the results obtained during a proof loading exercise undertaken prior to the bridge opening. It is shown that a vibration monitoring system based on consumer-grade digital camera can indeed provide an economically viable alternative to monitoring the complex time-evolving dynamic behaviour patterns of large-scale bridges.

Industrial construction projects involve complex interactions among uncertainty, risk, schedule, and cost, motivating the development of advanced analytical and decision-support methodologies. This study systematically reviews methodological approaches to risk assessment and performance management in industrial construction. Following the PRISMA 2020 guidelines, searches of the Scopus and Web of Science databases identified 234 records, of which 56 journal articles published between 2011 and 2025 satisfied the predefined eligibility and quality criteria. The selected studies were classified according to methodological family, uncertainty representation, and the degree of time–cost–risk integration. Four dominant methodological families were identified: multicriteria and fuzzy decision-making, probabilistic and simulation-based modeling, optimization, and artificial intelligence and machine learning. Twenty-seven studies assessed risk independently of time and cost, whereas only four jointly modeled all three dimensions. Probabilistic and optimization-based methods demonstrated the highest level of integrated analysis, while most machine-learning approaches remained prediction-oriented and most existing models were static rather than adaptive. Based on this synthesis, the review proposes a unified analytical taxonomy and a research agenda for integrated decision-support frameworks that combine dynamic uncertainty updating, predictive analytics, and multi-objective optimization. The findings highlight important methodological gaps and provide a structured foundation for developing mathematically rigorous, adaptive models that support robust decision-making in complex industrial construction environments.

Industrial construction projects involve complex interactions among uncertainty, risk, schedule, and cost, motivating the development of advanced analytical and decision-support methodologies. This study systematically reviews methodological approaches to risk assessment and performance management in industrial construction. Following the PRISMA 2020 guidelines, searches of Scopus and Web of Science identified 234 records, of which 56 articles published in Q1-ranked journals between 2011 and 2025 met the pre-specified eligibility criteria. The selected studies were classified according to methodological family, uncertainty representation, and the degree of time–cost–risk integration. Four dominant methodological families were identified: multicriteria and fuzzy decision-making, probabilistic and simulation-based modeling, optimization-based planning and resource allocation, and data-driven and artificial intelligence methods. Twenty-seven studies assessed risk independently of time and cost, whereas only four jointly modeled all three dimensions. Probabilistic and optimization-based methods demonstrated the highest level of integrated analysis, while most machine-learning approaches remained prediction-oriented and most existing models were static rather than adaptive. Based on this synthesis, the review proposes a unified analytical taxonomy and a research agenda for integrated decision-support frameworks that combine dynamic uncertainty updating, predictive analytics, and multi-objective optimization. The findings identify methodological gaps and provide a foundation for adaptive models supporting robust decision-making in complex industrial construction environments.