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.

As safety is a top priority in mission-critical engineering applications, uncertainty quantification emerges as a linchpin to the successful deployment of AI models in these high-stakes domains. In this article, we seamlessly encode a simple and principled uncertainty quantification module spectral-normalized neural Gaussian process (SNGP) into GoogLeNet to detect various defects in steel wire ropes (SWRs) accurately and reliably. To this end, the developed methodology consists of three coherent steps. In the first step, raw magnetic flux leakage (MFL) signals in waveform associated with normal and defective SWRs that are manifested in the number of broken wires are collected via a dedicated experimental setup. Next, the proposed approach utilizes Gramian angular field to transform the MFL signal in 1-D time series into 2-D images while preserving key spatial and temporal structures in the data. Third, built atop the backbone of GoogLeNet, we systematically integrate SNGP by adding the spectral normalization (SN) layer to normalize the weights and replacing the output layers with a Gaussian process (GP) in the main network and auxiliary classifiers of GoogLeNet accordingly, where SN enables to preserve the distance in data transformation and GP makes the output layer of neural network distance aware when assigning uncertainty. Comprehensive comparisons with the state-of-the-art models highlight the advantages of the developed methodology in classifying SWR defects and identifying out-of-distribution (OOD) SWR instances. In addition, a thorough ablation study is performed to quantitatively illustrate the significant role played by SN and GP in the principledness of the estimated uncertainty toward detecting SWR instances with varying OODness.

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.