Application of probabilistic tools to extend load test design of bridges prior to opening

A Proposal to Facilitate Mandatory Bridge Load Tests with Artificial Neural Network Analyses Using a Digital Data Aggregation Platform

“…) Due to the regressive nature of the network, the activation function in the output layer was omitted. After the analyzes and taking into account the preliminary nature of the examples, to predict the average deflection of the girders in static tests (example "A"), training parameters were selected in the form of: (1) directly loaded span length, (2) skew value, (3) axial moment inertia of the girders, (4) the width of the road, (5,6) the widths of the sidewalks, ( 7) the theo-…”

“…Importantly, the authors' proposal envisions a process that is in line with the standards and trends of modern research literature [8,27,30,31], in which it is clearly emphasized that it is worth enriching the traditional deterministic approach to the load test designs through a probabilistic approach and analysis of large amounts of data (so-called "Big Data"). Single, usually uncalibrated deterministic values are then replaced with appropriate -probabilistically defined -intervals of the expected values of the mechanical response of the structure based on a reliable analysis of sensitivity and uncertainty [32], (Fig. 2).…”

“…It is worth to note that Fig. 2 presents very simplified version of the above mentioned procedure, thus the reader is encouraged to read the source study [32].…”

“…These measurements are performed at different stages in the life cycle of the structure: a) during the construction phase -e.g. load testing of foundation piles [5], b) directly before admission to service -as mandatory static and dynamic test loading of road and rail bridges [2,6,7] c) during service life of the bridge -e.g. when facing a renovation decision, as part of an expert opinion on the technical condition or when it is necessary to increase the load capacity of the structure due to increasing operational requirements [8,9].…”

Influence of coupling factors on structural reliability based on polynomial response surface optimization model

“…4,5 At the same time, the combined effect of external random factors and operating condition parameters will directly affect the damage process, 6 and become an important factor affecting the operation safety of equipment. 7,8 Therefore, quantitatively evaluate the relationship between influencing factors and structure reliability indicators is very important for further ensure the operation safety of equipment, which has become the research focus in the field of reliability. 9,10 However, for industrial applications, there will also be internal coupling effect between the influencing factors, which will have a composite impact on the reliability index.…”