Seismic performance evaluation of recycled aggregate concrete-filled steel tubular columns with field strain detected via a novel mark-free vision method

Prediction of thermo-mechanical properties of rubber-modified recycled aggregate concrete

Fatigue bond behaviour between basalt fibre-reinforced polymer bars and seawater sea-sand concrete

Electrically conductive cementitious composites (ECCC) possesses numerous virtues including low resistivity and high strain sensitivity, which can be applied as a conductive sensor to monitor structural health. This study produced ECCC with three conductive ingredients, consisting of graphite powder (GP), ground granulated blast-furnace slag (GGBS), and steel slag (SS). The uniaxial compressive strength (UCS), flexural strength (FS), and electrical conductivity were investigated, which showed that the GP enhanced the conductivity more remarkably than the other conductive materials . However, it simultaneously reduced the UCS and FS of ECCC. Also, ECCC samples containing SS had higher FS and conductivity than that containing GGBS. To overcome the challenge of excessive variables, this study introduced an artificial-intelligence (AI) based multi-objective optimisation (MOO) model with 252 samples for the FS test and 336 samples for the resistivity experiment. The support vector regression (SVR) was trained with hyperparameters tuned by the beetle antennae search (BAS). The high correlation coefficients (0.981) were achieved on both test sets. The BAS-SVR model acted as the objective function to develop the multi-objective beetle antennae search algorithm (MOBAS-SVR). The Pareto front of a tri-objective mixture optimisation design for ECCC (cost, FS, and resistivity) was successfully obtained as a design reference. Furthermore, sensitivity research was implemented to comprehend the importance of the variables for the FS and electrical resistivity.

Multi-objective optimisation of a graphite-slag conductive composite applying a BAS-SVR based model

Waste glass is promising to be recycled and reused in construction for sustainability. Silicon dioxide is the main component of glass, however, its pozzolanic activity is latent mainly due to its stable silica tetrahedron structure. To excite the activation of waste glass, chemical activation and mechanical grinding of waste glass powder (WGP) were investigated. As the supplementary, hydrothermal and combined (mechanical-chemical-hydrothermal) treatments were conducted on part of the WGP samples. The unconfined compression strength (UCS), expansion caused by alkali–silica reaction (ASR), and the microstructural morphology of WGP were investigated. The results showed the dosage threshold (around 2%) of the chemical activators (alkali and sodium sulfate) and the combined activation were optimal. Besides, a firefly algorithm (FA) based multi-objective optimisation model (MOFA) was applied to seek the Pareto fronts based on three objectives: UCS, ASR expansion, and Cost of mixture proportion. The objective functions of UCS and expansion were established through training the machine learning (ML) models where FA was used to tune the hyperparameters. The cost was calculated by a polynomial function. The ultimate values of root mean square error (RMSE) and correlation coefficient (R) showed the robustness of the ML models. Moreover, the Pareto fronts for mortars containing 300 μm and 75 μm WGPs were successfully obtained, which contributed to the practical application of waste glass in mortar production. In addition, the sensitivity analysis was conducted to rank the importance of input variables. The results showed that curing time, activator's content, and WGP particle size were three essential parameters. 

Wanhui Feng

Papers

Fracture behavior of a sustainable material: Recycled concrete with waste crumb rubber subjected to elevated temperatures