Jie Li's scientific contributions
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Publications (5)
Construction repairs have used fiber-reinforced cement mortar (FRCM). Concrete and FRCM bond strength usually outweigh mechanical criteria. Nevertheless, testing complex bonds like the FRCM and concrete bond takes time, money, and errors. This study employed fuzzy logic (FL) based on the adaptive neuro-fuzzy inference system (ANFIS) to simplify and...
In this study, we developed an ANFIS-GA-PSO hybrid model for predicting the shear strength of concrete beams. Predicting the shear strength of concrete beams before construction is crucial in evaluating the structure's ability to withstand external forces such as floods and earthquakes. Building robust structures is critical in geotechnical enginee...
There have been various studies on fly ash-containing concrete that focuses on foretelling the properties of hardened concrete. However, little research has been conducted to forecast the characteristics of freshly mixed and cured self-compacting concrete (SCC). In the present article, the goal was to construct a network that estimates SCC both bef...
This article assess the precise estimation of the hysteresis loop of reinforced concrete (RC) beams in distinct failure cases to verify inelastic seismic beam function. Any test failure in RC frame columns is able to produce hysteresis curves in low cyclic repeat load that follows the analysis of the hysteretic behavior of the frame columns. In thi...
Curved panels are used for ships due to complex situations that can be under external loading. So, it is very important to know about the stability related to the curved system under Low-Velocity Impact (LVI). To improve the stability of this kind of structure, graphene oxide powders (GOPs) are added to its matrix. In addition, the contact force be...
Citations
... ANFIS is a type of hybrid intelligent system that integrates the characteristics of fuzzy logic and ANNs to create a model that can be applied for different applications, including prediction, modeling, and control. ANFIS is designed to learn from input-output training data and construct a FIS with the use of adaptive techniques such as gradient descent and least-squares prediction [25][26]. ...
... This research shows that ANIFS is very good for predicting volatile data and spikes, such as rainfall data. Several studies have produced the same conclusions about the ANFIS method (Li et al., 2023;Saleh et al., 2023;Samantaray et al., 2023;Sangar et al., 2024). ...
... In addition to FEM, civil engineering researchers have increasingly employed ML techniques to address intricate non-linear problems (e.g., Noureldin et al. 2023;Yang et al. 2023;Li et al. 2023). For example, several studies confirmed the effectiveness of ML algorithms in geotechnical applications, such as soil property prediction, site characterization, liquefaction potential assessment, landslide prediction, and slope stability analysis (e.g., Ahmad et al. 2021;Seyrek and Topcu 2022;Dehghanbanadaki 2021;Huqqani et al. 2023;Onyelowe et al. 2023;Phoon and Zhang 2023;Xu et al. 2022;Zhang et al. 2023;Han et al.2023). ...
... Civil engineering structures generally exhibit hysteretic behavior, where the responses of the structures undergo in the inelastic interval back and forth and they depend upon not only the current structural states but also upon the former ones, while they are excited by strong dynamic loadings such as earthquake ground motions (Zhu et al., 2000;. Thus far, various models portraying the hysteretic behavior of such nonlinear structures were addressed, which are classified into the parametric model (Bouc, 1967;Wen, 1976;Clough and Johnston, 1966) and the non-parametric model (Delgado-Trujillo et al., 2023;Yan et al., 2023). As for the parametric model which fundamentally depends on mathematical equations associated with the characteristics of the structures, two generic methods are widely deployed to represent the hysteretic behavior of structures, that is, the bilinear elastoplastic model (Datta, 2010;Iwan, 1961) and the Bouc-Wen differential model (Ma et al., 2004;Foliente, 1995). ...
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