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Accurately predicting the payload movement and ensuring efficient control during dynamic tower crane operations are crucial for crane safety, including the ability to predict payload mass within a safe or normal range. This research utilizes deep learning to accurately predict the normal and abnormal payload movement of tower cranes. A scaled-down tower crane prototype with a systematic data acquisition system is built to perform experiments and data collection. The data related to 12 test case scenarios are gathered, and each test case represents a specific combination of hoisting and slewing motion and payload mass to counterweight ratio, defining tower crane operational variations. This comprehensive data is investigated using a novel attention-based deep neural network with Tree-Structured Parzen Estimator optimization (TPE-AttDNN). The proposed TPE-AttDNN achieved a prediction accuracy of 0.95 with a false positive rate of 0.08. These results clearly demonstrate the effectiveness of the proposed model in accurately predicting the tower crane payload moving condition. To ensure a more reliable performance assessment of the proposed AttDNN, we carried out ablation experiments that highlighted the significance of the model's individual components. [ABSTRACT FROM AUTHOR]

The performance of structures degrades during their service life due to deterioration and extreme events, compromising the social development and economic growth of structure and infrastructure systems. Buildings and bridges play a vital role in the socioeconomic development of the built environment. Hence, it is essential to understand existing tools and methodologies to efficiently model the performance of these structures during their life cycle. In this context, this paper aims to explore the existing literature on the life-cycle performance modeling, assessment, enhancement, and decision making of buildings and bridge infrastructure systems under deterioration and extreme events for a sustainable and resilient built environment. The main objectives are to (1) systematically review the existing literature on life-cycle performance modeling of buildings and bridges based on the PRISMA methodology, (2) provide a bibliometric analysis of the systematically assessed journal articles, (3) perform an analysis of the included articles based on the identified components of life-cycle performance modeling, and (4) provide a discussion on the utilized tools, techniques, methodologies, and frameworks for buildings and bridge infrastructure systems in the life-cycle context. The provided systematic literature review and subsequent discussions could provide an overview to the reader regarding the individual components and existing methodologies of life-cycle performance management under deterioration and extreme events. [ABSTRACT FROM AUTHOR]

It is of vital importance to incorporate sustainability and resilience in the performance-based decision-making of civil infrastructure under seismic hazard. However, a performance-based engineering framework utilizing component-level approach, integrating seismic loss, sustainability, and resilience in a multi-criteria decision-making (MCDM) is not yet extensively developed particularly for retrofit selection in a long-term perspective. This paper introduces a framework utilizing performance-based approach to couple seismic loss, sustainability, and resilience in decision-making framework for selection of different retrofit alternatives. A component-based probabilistic approach is developed for the seismic loss, sustainability, and resilience assessment in a long-term perspective. The resulting social, economic, and environmental consequences are converted to expected annual consequences (EACs) by considering the full range of seismic hazards, and are utilized as a multi-criterion in the technique for order preference by similarity to ideal solution (TOPSIS). The proposed long-term performance-based multi-criteria decision-making (PB-MCDM) framework considers five consequences, which include cost, casualties, equivalent carbon emissions, embodied energy, and repair time. Based on the illustrative example, it may be concluded that incorporating seismic sustainability and resilience in PB-MCDM approach in a long-term perspective can provide ideal solutions and better decision-making against retrofit alternatives. [ABSTRACT FROM AUTHOR]

Recent earthquakes have highlighted additional losses due to the lack of resilience of damaged structures. Environmental impact, as performance indicator, has also received increased attention within performance-based earthquake engineering. In this article, a combined probabilistic framework is proposed to assess seismic risk, sustainability, and resilience of a non-ductile reinforced concrete frame structure. The framework utilizes three-dimensional inelastic fiber-based numerical modeling approach to develop limit states associated with performance levels. The decision variables (i.e. repair cost, downtime, and equivalent carbon emissions) are quantified at both component level and system level and are compared considering seismic risk, sustainability, and resilience. In addition, the proposed approach considers uncertainties in the building performance and consequence functions of structural and non-structural components. Fast-track and slow-track schemes are utilized as a repair strategy and probabilistic resilience is quantified given the investigated time period. The proposed approach can aid the development of the next generation of performance-based engineering incorporating both resilience and sustainability. [ABSTRACT FROM AUTHOR]