Wire rope is a vital component of every crane. Wire rope faults are related to the operation, fabrication environment, etc., and the prevalent mode of failure is fatigue. The aim of this study is to develop an advanced tower crane applicable to wire rope with integrated reinforcement. Steel wire ropes are superiorly used in several tower crane applications, but they may create certain failures such as less fatigue and wear-resistant. In this study, steel wires are strengthened by granite and Zinc oxide (ZnO) reinforcement. Two sets of wire ropes are prepared as complete and partial reinforcement of steel wire with seven strands and 15 wires. The failure tests such as hardness, wear analysis, tensile strength, and fatigue life are optimized using hybrid Harris Hawk optimization-based Particle swarm Optimization (Hybrid HHO-PSO). Besides, the experimented wire rope performances are predicted using hybrid Artificial Neural Network based HHO (Hybrid ANN-HHO). Fully reinforced wire ropes provide better performances for both experimented and optimization behaviors. This provides 1818 MPa of maximum tensile strength, 0.23 mm of minimal wear depth, and 3.38x104 times better fatigue life. In the HHO-PSO optimization method, the obtained better tensile strength is 1822 MPa, wear depth is 0.66 mm, and Fatigue life is 3.57 x104 times. Besides, from the predicted outcomes, ANN-HHO provides a minimal error value than the ANN approach. The result of this study will open up different ways for the advancement of wire rope in tower crane application by improving its load bearing capacity. The outcomes from this research can be practically applicable for increasing the load bearing capacity of the tower crane without increasing the number of wires and strands in the wire rope.

中文翻译：

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基于混合 HHO-PSO 和 ANN-HHO 算法的塔式起重机增强钢丝绳的优化和预测


钢丝绳是每台起重机的重要组成部分。钢丝绳故障与操作、制造环境等有关，普遍的失效模式是疲劳。本研究的目的是开发一种适用于钢丝绳的先进塔式起重机，具有集成加固。钢丝绳在多种塔式起重机应用中得到了很好的使用，但它们可能会产生某些故障，例如疲劳和耐磨性降低。在本研究中，钢丝通过花岗岩和氧化锌 （ZnO） 加固进行加固。制备两组钢丝绳，作为 7 股和 15 根钢丝的完整和部分增强。硬度、磨损分析、抗拉强度和疲劳寿命等失效测试使用基于 Harris Hawk 优化的混合粒子群优化 （Hybrid HHO-PSO） 进行优化。此外，使用基于混合人工神经网络的 HHO （Hybrid ANN-HHO） 预测实验钢丝绳的性能。完全增强的钢丝绳为实验和优化行为提供了更好的性能。这提供了 1818 MPa 的最大抗拉强度、0.23 mm 的最小磨损深度和 3.38x104 倍的疲劳寿命。在 HHO-PSO 优化方法中，获得的较好的抗拉强度为 1822 MPa，磨损深度为 0.66 mm，疲劳寿命为 3.57 x104 倍。此外，从预测结果来看，ANN-HHO 提供的误差值比 ANN 方法最小。本研究的结果将通过提高钢丝绳的承载能力，为钢丝绳在塔式起重机应用中的发展开辟不同的途径。这项研究的结果实际上适用于在不增加钢丝绳中的钢丝和股数的情况下提高塔式起重机的承载能力。