The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and Deep Neural Network model (artificial neural network (ANN) with double and triple hidden layers). The database of 330 samples collected for the training model contains many important parameters, i.e., section type (circle or square), corner radius rc, unconfined concrete strength fco, thickness nt, the elastic modulus of fiber Ef , the elastic modulus of mortar Em. The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy. The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models. Furthermore, the results also reveal that the unconfined compressive strength of concrete, type of fiber mesh for FRCM, type of section, and the corner radius ratio, are the most significant input variables in the efficiency of FRCM confinement prediction. The performance of the proposed ANN models (including double and triple hidden layers) had high precision with R higher than 0.93 and RMSE smaller than 0.13, as compared with other models from the literature available.
Due to recent advances in the field of artificial neural networks (ANN) and the global sensitivity analysis (GSA) method, the application of these techniques in structural analysis has become feasible. A connector is an important part of a composite beam, and its shear strength can have a significant impact on structural design. In this paper, the shear performance of perfobond rib shear connectors (PRSCs) is predicted based on the back propagation (BP) ANN model, the Genetic Algorithm (GA) method and GSA method. A database was created using push-out test test and related references, where the input variables were based on different empirical formulas and the output variables were the corresponding shear strengths. The results predicted by the ANN models and empirical equations were compared, and the factors affecting shear strength were examined by the GSA method. The results show that the use of ANN model optimization by GA method has fewer errors compared to the empirical equations. Furthermore, penetrating reinforcement has the greatest sensitivity to shear performance, while the bonding force between steel plate and concrete has the least sensitivity to shear strength.
The prediction of structural performance plays a significant role in damage assessment of glass fiber reinforcement polymer (GFRP) elastic gridshell structures. Machine learning (ML) approaches are implemented in this study, to predict maximum stress and displacement of GFRP elastic gridshell structures. Several ML algorithms, including linear regression (LR), ridge regression (RR), support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), category boosting (CatBoost), and light gradient boosting machine (LightGBM), are implemented in this study. Output features of structural performance considered in this study are the maximum stress as f1(x) and the maximum displacement to self-weight ratio as f2(x). A comparative study is conducted and the Catboost model presents the highest prediction accuracy. Finally, interpretable ML approaches, including shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions. SHAP is employed to describe the importance of each variable to structural performance both locally and globally. The results of sensitivity analysis (SA), feature importance of the CatBoost model and SHAP approach indicate the same parameters as the most significant variables for f1(x) and f2(x).
Shear failure of slender reinforced concrete beams without stirrups has surely been a complicated occurrence that has proven challenging to adequately understand. The primary purpose of this work is to develop machine learning models capable of reliably predicting the shear strength of non-shear-reinforced slender beams (SB). A database encompassing 1118 experimental findings from the relevant literature was compiled, containing eight distinct factors. Gradient Boosting (GB) technique was developed and evaluated in combination with three different optimization algorithms, namely Particle Swarm Optimization (PSO), Random Annealing Optimization (RA), and Simulated Annealing Optimization (SA). The findings suggested that GB-SA could deliver strong prediction results and effectively generalizes the connection between the input and output variables. Shap values and two-dimensional PDP analysis were then carried out. Engineers may use the findings in this work to define beam's geometrical components and material used to achieve the desired shear strength of SB without reinforcement.
This paper addresses the potential use of Sugar Cane Bagasse Ash (SCBA) as a pozzolanic material for partial cement replacement in concrete mixtures. Cement mortars containing SCBA having five different particle size distributions at a replacement rate of 20% by weight were used to study the chemical and physical pozzolanic properties of SCBA. The durability of SCBA replaced mortars was also evaluated. SCBA with 0% retained on sieve No. 325 was used to replace 20% by weight of cement and create mortar specimens that were subjected to sulfuric acid attack of varying concentrations (1%−3% by weight of water). The tested samples were observed to check visual distortion, mass loss, and compressive strength loss at 1, 7, 14, 28, and 56 d of acidic exposure, and the results were compared to those for the control sample, that was lime water cured, at the same ages. The SCBA sets were found to meet the requirements for pozzolan class N specified by ASTM C 618. Mortars containing SCBA with 0% or 15% retention produced better compressive strength than the control mortars after 28 d. Additionally, X-ray fluorescence and X-ray diffraction analysis showed that the SCBA had favorable chemical properties for a pozzolanic material. Furthermore, SCBA replaced samples at all ages showed improved resistance against acidic attack relative to that of the control mortars. Maximum deterioration was seen for 3% concentrated solution. This study’s findings demonstrated that SCBA with an appropriate fineness could be used as a pozzolanic material, consistently with ASTM C 618.
Thermal energy storage recycled powder mortar (TESRM) was developed in this study by incorporating paraffin/recycled brick powder (paraffin/BP) composite phase change materials (PCM). Fourier transform infrared and thermogravimetric analysis results showed that paraffin/BP composite PCM had good chemical and thermal stability. The onset melting temperature and latent heat of the composite PCM were 46.49 °C and 30.1 J·g−1. The fresh mortar properties and hardened properties were also investigated in this study. Paraffin/BP composite PCM with replacement ratio of 0%, 10%, 20%, and 30% by weight of cement were studied. The results showed that the static and dynamic yield stresses of TESRM were 699.4% and 172.9% higher than those of normal mortar, respectively. The addition of paraffin/BP composite PCM had a positive impact on the mechanical properties of mortar at later ages, and could also reduce the dry shrinkage of mortar. The dry shrinkage of TESRM had a maximum reduction about 26.15% at 120 d. The thermal properties of TESRM were better than those of normal mortar. The thermal conductivity of TESRM was 36.3% less than that of normal mortar and the heating test results showed that TESRM had good thermal energy storage performance.
The present study proposes the mix design method of Fly Ash (FA) based geopolymer concrete using Response Surface Methodology (RSM). In this method, different factors, including binder content, alkali/binder ratio, NS/NH ratio (sodium silicate/sodium hydroxide), NH molarity, and water/solids ratio were considered for the mix design of geopolymer concrete. The 2D contour plots were used to setup the mix design method to achieve the target compressive strength. The proposed mix design method of geopolymer concrete is divided into three categories based on curing regime, specifically one ambient curing (25 °C) and two heat curing (60 and 90 °C). The proposed mix design method of geopolymer concrete was validated through experimentation of M30, M50, and M70 concrete mixes at all curing regimes. The observed experimental compressive strength results validate the mix design method by more than 90% of their target strength. Furthermore, the current study concluded that the required compressive strength can be achieved by varying any factor in the mix design. In addition, the factor analysis revealed that the NS/NH ratio significantly affects the compressive strength of geopolymer concrete.
An innovative composite deck system has recently been proposed for improved structural performance. To study the fatigue behavior of a steel-concrete composite bridge deck, we took a newly-constructed rail-cum-road steel truss bridge as a case study. The transverse stress history of the bridge deck near the main truss under the action of a standard fatigue vehicle was calculated using finite element analysis. Due to the fact that fatigue provision remains unavailable in the governing code of highway concrete bridges in China, a preliminary fatigue evaluation was conducted according to the fib Model Code. The results indicate that flexural failure of the bridge deck in the transverse negative bending moment region is the controlling fatigue failure mode. The fatigue life associated with the fatigue fracture of steel reinforcement is 56 years. However, while the top surface of the bridge deck concrete near the truss cracks after just six years, the bridge deck performs with fatigue cracks during most of its design service life. Although fatigue capacity is acceptable under design situations, overloading or understrength may increase its risk of failure. The method presented in this work can be applied to similar bridges for preliminary fatigue assessment.
In this study, fire tests of four single-section scaled-down utility tunnels were conducted. By analyzing temperature and structural responses of the utility tunnel throughout the fire exposure, the effects on the fire behavior of two different construction methods, cast-in-situ and prefabricated, and of two different materials, ordinary concrete and full lightweight concrete, were explored. The results of the study showed that the shear failure of the cast-in-situ utility tunnel occurred at the end of the top or bottom plate, and the failure of the prefabricated utility tunnel occurred at the junction of the prefabricated member and post-cast concrete. As the temperature increased, the temperature gradient along the thickness direction of the tunnel became apparent. The maximum temperature difference between the inner and outer wall surfaces was 531.7 °C. The highest temperature occurred in the cooling stage after stopping the heating, which provided a reference for the fire protection design and rescue of the utility tunnel. The displacement of the top plate of the prefabricated utility tunnel was 16.8 mm, which was 41.8% larger than that of the cast-in-situ utility tunnel. The bearing capacities of the ordinary concrete utility tunnel and full lightweight concrete utility tunnel after the fire loss were 27% and 16.8%, respectively. The full lightweight concrete utility tunnel exhibited good ductility and fire resistance and high collapse resistance.
