A promising substitute for regular concrete is geopolymer concrete. Engineering mechanical parameters of geopolymer concrete, including compressive strength, are frequently measured in the laboratory or in-situ via experimental destructive tests, which calls for a significant quantity of raw materials, a longer time to prepare the samples, and expensive machinery. Thus, to evaluate compressive strength, non-destructive testing is preferred. Therefore, the objective of this research is to develop an artificial neural network model based on the results of destructive and non-destructive tests to assess the compressive strength of geopolymer concrete without needing further destructive tests. According to the artificial neural network analysis developed in this study, the compressive strength of geopolymer concrete can be predicted rather accurately by combining the results of the non-destructive with R
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The tunnel lining systems act as lines of defence against the forces and geotechnical situations. The use of precast concrete tunnel linings (PCTLs) has been escalating due to its effective and economical installation process. The tunnels usually suffer from the premature deterioration due to corrosion of the reinforcement and thus need maintenance. Corrosion leads to the distress in PCTL leading to the cracking and finally the scaling of concrete. This study aims to assess the structural durability performance of reactive powder concrete (RPC) as the material of tunnel lining segments compared to reinforced concrete (RC) and high performance concrete (HPC). The numerical findings indicated that the maximum load capacity of PRC-PCTL segments was greater than that of the corresponding RC and HPC segments. Regarding the findings, PRC is a very significant option for conventional segments. The high strength of PRC can decrease the thickness of the PCTL segments, resulting in the decreased material cost. Also, PRC-PCTL segments can eliminate the laborious and costly production of RC segments and mitigate the corrosion damage and thus enhance the service life of lining segments.
Tuned mass dampers (TMDs) as vibration-mitigating devices are widely used in structures to reduce their displacement response under dynamic forces. Through a novel dolphin echolocation (DE) algorithm, this paper provides optimum tuning of TMD parameters. Developing some features of this algorithm results in a faster convergence to the optimum solution. Besides, grey wolf and whale optimization algorithms (GWO and WOA), as two other nature-based meta-heuristic algorithms, are employed in this problem. The modified DE illustrates a more optimum design of TMD's parameters rather than GWO and WOA. The code has been verified by a sample structure from the literature and then applied to a high-rise forty-story structure under strong ground motions. The numerical results reveal that the optimum TMD is viable in attenuating the structural responses, including relative displacements and absolute accelerations under different earthquake excitations. For instance, in the high-rise structure, the modified DE, GWO, and WOA reduce the maximum displacements up to 45%, 43%, and 38%, respectively. Moreover, the algorithms, specifically the modified DE, propose more cost-effective designs in comparison with previous studies in the literature by introducing smaller TMD parameters.
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