Reliability-based life-cycle cost design of asphalt pavement using artificial neural networks

Xin, Jiyu; Akiyama, Mitsuyoshi; Frangopol, Dan M.; Zhang, Mingyang; Pei, Jianzhong; Zhang, Jiupeng

doi:10.1080/15732479.2020.1815807

Cited by 23 publications

(3 citation statements)

References 42 publications

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“…Experts and scholars in the field of safety risk warning have done a lot of research and obtained a lot of theoretical research results. Still, there is little research on the characteristics of the flight control of the edge computing UAV nest station and the features of the control operation of the region, combined with the corresponding research methods to establish a risk factor index system and obtain a safety risk warning model for the control operation of the UAV nest station [24].…”

Section: Related Workmentioning

confidence: 99%

Spatial Information-Aware Flight Safety Forecasting Model for Unmanned Aerial Vehicles Based on Deep Learning and Grey Analysis

Pan,

Wang,

et al. 2024

IEEE Access

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Ensuring flight safety for unmanned aerial vehicles (UAVs) is a critical concern, necessitating effective mathematical modeling for safety forecasting in both academic and industrial contexts. This study addresses this need by combining the capabilities of deep neural networks and grey analysis to create a comprehensive mathematical modeling approach focused on spatial information service (SIS). The paper introduces a novel spatial information-aware flight security forecasting model for UAVs, emphasizing the transformative impact of the new methodology. Traditionally, factors influencing flight safety are identified and formulated based on SIS chain technology, SIS management rules, SIS business processes, and spatial SIS chain verification. To address the challenges posed by significant data volatility and missing data in the sample dateset, a non-equally spaced GM (1,1) model with an approximate nonsimultaneous exponential law series is developed for prediction. Subsequently, multiple influencing factors are encoded and input into a specific BP neural network structure. The paper concludes with simulation experiments to evaluate the proposed model. The results of the simulation analysis demonstrate that the integration of deep learning and grey analysis in the proposed model effectively recognizes flight security risks with high efficiency. This underscores the transformative potential of the new approach in enhancing UAV flight safety forecasting.

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Section: Related Workmentioning

confidence: 99%

Spatial Information-Aware Flight Safety Forecasting Model for Unmanned Aerial Vehicles Based on Deep Learning and Grey Analysis

Pan,

Wang,

et al. 2024

IEEE Access

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“…Given the many uncertainties involved in the prediction, time-variant reliability analysis is often used to consider detrimental effects due to progressive deterioration. Xin et al [14] put forward an integrated optimization scheme based on reliability, taking into account the life-cycle cost. Complex and non-linear problems can be conducted using the powerful capacity of Artificial Neural Networks (ANNs), which are carried out to forecast the performance of asphalt pavement on the basis of the training information chosen from the long-term pavement performance plan.…”

Section: Introductionmentioning

confidence: 99%

Life-Cycle Seismic Reliability Analysis of a Railway Cable-Stayed Bridge Considering Material Corrosion and Degradation

Zhang,

Hu,

Liu

et al. 2023

Buildings

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To study the life-cycle seismic reliability analysis (SRA) of cable-stayed bridges (CSBs) taking into account chloride-induced corrosion and degradation of components, an actual railway CSB with uncertainties in structural geometry and material corrosion coefficients was employed in this investigation, and time-dependent models of CSB components at different service times were studied. Based on the OpenSees batch program, we adapted a mass numerical computation to obtain time-dependent non-linear seismic response and probability density function (PDF) of response via the multiplier dimensional-reduction method (MDRM) and the maximum entropy method with fractional moments (FM-MEM). Next, the time-dependent failure possibility of every component and the association coefficient between the failure modes of different parts were acquired. In the end, the product of the conditional marginal (PCM) approach was employed to obtain the life-cycle failure possibility of the CSB system. The results showed that the system failure possibility of the CSB in a corrosive environment increases significantly with increasing servicing time.

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“…Consequently, research has been conducted over the past recent years on reducing the environmental and economic impacts of structures along their life cycle. Efforts have been made on optimising infrastructure designs to find cost-effective solutions [4][5][6], as well as to find the optimal maintenance intervals from an economic perspective [7][8]. Research has also been recently conducted on finding designs that minimise environmental impacts derived from construction [9] and maintenance of infrastructures [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Sustainability life cycle design of bridges in aggressive environments considering social impacts

Navarro¹,

Yepes²,

Martí³

2021

Int. J. CMEM

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The establishment of the Sustainable Development Goals in 2015 claims for a deep paradigm shift in the way infrastructure structures are conceived. The evaluation of the impacts derived from the construction, the service and the end-of-life stages of an infrastructure is consequently in the spotlight of the research community. Being the construction sector as one of the main stressors of the environment, great attention has been recently paid to the structural design from the economic and the environmental point of view. However, sustainability requires to consider the social dimension as well. The evaluation of the social impacts of products is still at a very early stage of development, so the inclusion of social aspects in the design of structures is often overlooked. In this study, a comparison of life cycle assessment results is conducted on seven different design alternatives for a bridge in a coastal environment. Two approaches are followed: the first approach considers the economic and the environmental aspects of each design and the second approach includes the several social impacts specifically developed for the assessment of infrastructures. These social impacts account for four stakeholders, namely workers, consumers, local community and society. Results show that the inclusion of social aspects shall lead to different preferred options when compared with conventional, two-dimensional approaches. Here, the design with silica fume added concrete performs 11% better from a sustainability point of view when compared with the best solution resulting from a conventional assessment.

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Reliability-based life-cycle cost design of asphalt pavement using artificial neural networks

Cited by 23 publications

References 42 publications

Spatial Information-Aware Flight Safety Forecasting Model for Unmanned Aerial Vehicles Based on Deep Learning and Grey Analysis

Spatial Information-Aware Flight Safety Forecasting Model for Unmanned Aerial Vehicles Based on Deep Learning and Grey Analysis

Life-Cycle Seismic Reliability Analysis of a Railway Cable-Stayed Bridge Considering Material Corrosion and Degradation

Sustainability life cycle design of bridges in aggressive environments considering social impacts

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