Abstract

Ultra-high-performance fibre reinforced concrete (UHPFRC) is an advanced construction material that provides new opportunities in the future of the construction industry around the world. Among those new applications, rehabilitation, and seismic retrofitting of existing damaged or non-ductile concrete structures can be highlighted. The main objective of this paper is to optimise the hybrid blend of fibres that allows a previously optimised eco-friendly ultra-high-performance cementitious paste to achieve the ductility requirements for seismic retrofitting applications at lower costs. To meet this goal, two artificial neural network models (ANNs) were created to predict the energy absorption capacity (g) and maximum post-cracking strain (εpc). A total of 50 own experimental campaign data added to 550 published works throughout the world data were used for training purposes by using the R-code language. Once the models were trained and validated, a multi-objective optimisation was used to select the combination of fibres that achieved the limit values of g ≥ 50 kJ/m3 and εpc ≥ 0.3% considering cost constraints. The experimentally validated results indicated that the adequate blend of high strength steel micro-fibres and hooked end normal strength steel fibres fulfil the threshold values at a lower cost.

摘要

超高性能纤维增强混凝土 (UHPFRC) 是一种先进的建筑材料，为全球建筑业的未来提供了新机遇。在这些新应用中，可以突出现有受损或非延性混凝土结构的修复和抗震改造。本文的主要目的是优化纤维的混合混合物，使先前优化的环保超高性能水泥浆能够以较低的成本达到抗震改造应用的延展性要求。为了实现这一目标，创建了两个人工神经网络模型 (ANN) 来预测能量吸收能力 (g) 和最大开裂后应变 (εpc)。通过使用 R 代码语言，将总共 50 个自己的实验活动数据添加到全球 550 个已发表作品的数据中，用于训练目的。一旦模型经过训练和验证，考虑到成本限制，使用多目标优化来选择达到 g ≥ 50 kJ/m3 和 εpc ≥ 0.3% 的极限值的纤维组合。实验验证的结果表明，高强度钢微纤维和钩端正常强度钢纤维的充分混合以较低的成本满足阈值。