A hybrid machine learning model to estimate self-compacting concrete compressive strength

This study examined the feasibility of using the grey wolf optimizer (GWO) and artificial neural network (ANN) to predict the compressive strength (CS) of self-compacting concrete (SCC). The ANN-GWO model was created using 115 samples from different sources, taking into account nine key SCC factors. The validation of the proposed model was evaluated via six indices, including correlation coefficient (R), mean squared error, mean absolute error (MAE), IA, Slope, and mean absolute percentage error. In addition, the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots. The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS. Following that, an examination of the parameters impacting the CS of SCC was provided.     

Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive strength of concrete using rebound hammer field test

In this study, we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System (ANFIS) optimized by Shuffled Complex Evolution (SCE) on the one hand and ANFIS with Artificial Bee Colony (ABC) on the other hand. These were used to predict compressive strength (Cs) of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the laboratory. Field and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway, Vietnam were considered. The dataset was randomly divided into a 70:30 ratio, for training (70%) and testing (30%) of the hybrid models. Performance of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics: Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that both of the novel models depict close agreement between experimental and predicted results. However, the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete Cs. Thus, the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.     

Universal boosting ML approaches to predict the ultimate load capacity of CFST columns

Establishing a universal machine learning (ML) model in structural engineering is vital for understanding how various parameters, like geometry and material properties, influence a structure's behavior. This study aims to create a comprehensive ML model that considers the impact of different cross-sectional parameters on the ultimate load capacity (ULC) of concrete-filled steel tube (CFST) columns. This model assists engineers in making informed design decisions. The study employs a large dataset of 3094 data points with diverse geometric and material properties of CFST columns. After adjusting input features, robust boosting ML models (Catboost, LightGBM, and XGB) are meticulously fine-tuned using grid search and fivefold cross-validation. Monte Carlo simulation is used for further assessment. The results demonstrate that the most accurate XGB model delivers impressive accuracy, comparable to or better than existing literature models that focused on a single CFST column cross-section. The chosen XGB model is then utilized for feature importance analysis, local performance assessment, and sensitivity analysis through 1-D and 2-D partial dependence plots. These analyses help assess the input's contribution and effect on ULC prediction for CFST columns.     

Development of deep neural network model to predict the compressive strength of FRCM confined columns

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 r_c, unconfined concrete strength f_co, thickness n_t, the elastic modulus of fiber E_f , the elastic modulus of mortar E_m. 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.     

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

Fiber-reinforced self-compacting concrete (FRSCC) is a typical construction material, and its compressive strength (CS) is a critical mechanical property that must be adequately determined. In the machine learning (ML) approach to estimating the CS of FRSCC, the current research gaps include the limitations of samples in databases, the applicability constraints of models owing to limited mixture components, and the possibility of applying recently proposed models. This study developed different ML models for predicting the CS of FRSCC to address these limitations. Artificial neural network, random forest, and categorical gradient boosting (CatBoost) models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique. A database of 381 samples was created, representing the most significant FRSCC dataset compared with previous studies, and it was used for model development. The findings indicated that CatBoost outperformed the other two models with excellent predictive abilities (root mean square error of 2.639 MPa, mean absolute error of 1.669 MPa, and coefficient of determination of 0.986 for the test dataset). Finally, a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of FRSCC. The results showed that the cement content, testing age, and superplasticizer content are the most critical factors affecting the CS.     

Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network

In this study, an artificial neural networks study was carried out to predict the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives. This study is based on the determination of the variation of core compressive strength, water absorption and unit weight in curtain wall elements. One conventional concrete (vibrated concrete) and six different self-compacting concrete (SCC) mixtures with mineral additives were prepared. SCC mixtures were produced as control concrete (without mineral additives), moreover fly ash and limestone powder were used with two different replacement ratios (15% and 30%) of cement and marble powder was used with 15% replacement ratio of cement. SCC mixtures were compared to conventional concrete according to the variation of compressive strength, water absorption and unit weight. It can be seen from this study, self-compacting concretes consolidated by its own weight homogeneously in the narrow reinforcement construction elements. Experimental results were also obtained by building models according to artificial neural network (ANN) to predict the core compressive strength. ANN model is constructed, trained and tested using these data. The results showed that ANN can be an alternative approach for the predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives.     

自密实混凝土力学性能的试验研究

自密实混凝土自20世纪80年代在日本发明和应用以来，其应用领域逐渐扩展至世界许多国家。自密实混凝土也逐渐向着高强、轻质的方向发展。本文依据全计算法自行设计了一套CAO自密实混凝土的配比方案。随后通过试验测量了新拌混凝土的坍落度和坍落流动度以及不同龄期下该混凝土的抗压强度、劈裂强度、抗折强度和弹性模量。对这些基本力学参数进行了分析比较，并导出抗压强度和劈裂强度、抗压强度和抗折强度的近似线性关系武。而且还对自密实混凝土与同配比振捣成型混凝土的抗压强度进行比较。结果表明由于后者在振捣过程中出现离析现象，得到的抗压强度值低干前者。     

Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system optimized by nature-inspired algorithms

Concrete compressive strength prediction is an essential process for material design and sustainability. This study investigates several novel hybrid adaptive neuro-fuzzy inference system (ANFIS) evolutionary models, i.e., ANFIS–particle swarm optimization (PSO), ANFIS–ant colony, ANFIS–differential evolution (DE), and ANFIS–genetic algorithm to predict the foamed concrete compressive strength. Several concrete properties, including cement content (C), oven dry density (O), water-to-binder ratio (W), and foamed volume (F) are used as input variables. A relevant data set is obtained from open-access published experimental investigations and used to build predictive models. The performance of the proposed predictive models is evaluated based on the mean performance (MP), which is the mean value of several statistical error indices. To optimize each predictive model and its input variables, univariate (C, O, W, and F), bivariate (C–O, C–W, C–F, O–W, O–F, and W–F), trivariate (C–O–W, C–W–F, O–W–F), and four-variate (C–O–W–F) combinations of input variables are constructed for each model. The results indicate that the best predictions obtained using the univariate, bivariate, trivariate, and four-variate models are ANFIS–DE– (O) (MP= 0.96), ANFIS–PSO– (C–O) (MP= 0.88), ANFIS–DE– (O–W–F) (MP= 0.94), and ANFIS–PSO– (C–O–W–F) (MP= 0.89), respectively. ANFIS–PSO– (C–O) yielded the best accurate prediction of compressive strength with an MP value of 0.96.     

高温后混杂纤维混凝土抗压强度

通过混杂纤维混凝土试块的高温后抗压试验,分析了温度、纤维类别和纤维体积率、混凝土基体强度等级对混凝土高温后抗压强度的影响。结果表明:随着经历温度的升高,混杂纤维混凝土高温后的抗压强度及高温后与常温下抗压强度比在400℃之后下降幅度较大;适宜掺量的钢纤维(1%纤维体积率)和聚丙烯纤维(0.1%纤维体积率)能较好的提高混杂纤维混凝土高温后的抗压强度。在试验研究的基础上,建立了考虑温度、钢纤维和聚丙烯纤维体积率共同影响的高温后混杂纤维混凝土抗压强度计算模型,为纤维混凝土结构的抗火设计及灾后处理提供了理论依据。     

Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees and optimization algorithms

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.     

Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling with Relief algorithm

The consolidation coefficient of soil (C_v) is a crucial parameter used for the design of structures leaned on soft soi. In general, the C_v is determined experimentally in the laboratory. However, the experimental tests are time-consuming as well as expensive. Therefore, researchers tried several ways to determine C_v via other simple soil parameters. In this study, we developed a hybrid model of Random Forest coupling with a Relief algorithm (RF-RL) to predict the C_v of soil. To conduct this study, a database of soil parameters collected from a case study region in Vietnam was used for modeling. The performance of the proposed models was assessed via statistical indicators, namely Coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The proposal models were constructed with four sets of soil variables, including 6, 7, 8, and 13 inputs. The results revealed that all models performed well with a high performance (R² > 0.980). Although the RF-RL model with 13 variables has the highest prediction accuracy ( R² = 0.9869), the difference compared with other models was negligible (i.e., R² = 0.9824, 0.9850, 0.9825 for the cases with 6, 7, 8 inputs, respectively). Thus, it can be concluded that the hybrid model of RF-RL can be employed to predict C_v based on the basic soil parameters.     

Relation between hardness and compressive strength of polymer concrete

Experimental studies were made of the influence of mixing and casting techniques and of curing time upon the compressive strength and hardness of methyl methacrylate based polymer concrete. The Rockwell K and F scales were found to be suitable for the hardness determinations. Over a wide range of hardness and strength values, the average compressive strength (f_c) varied linearly with the average hardness (R_K) according to the relation f_c (psi) = 560 R_K - 12000, irrespective of casting and curing variables. The weaker materials exhibited greater variations in hardness. Hardness testing is found to provide a meaningful and convenient method for evaluating the quality of polymer concrete.     

Effect of silica fume and steel fibers on some properties of high-strength concrete

The main disadvantage of high-strength concrete is its highly brittle behavior and this can beovercome by adding fibers to the concrete. This would also improve some other mechanical properties of high-strength concrete such as tensile strength and compressive strength. These properties are not very well established for high-strength steel-fiber reinforced concrete (HSFRC) yet. In this study the influence of silica fume on the properties of HSFRC were investigated by using silica fume of two different percentages and three different hooked-end fibers namely, 30/0.50, 60/0.80 and 50/0.60 length/diameter (mm/mm). Fibers were added to concrete in three different volume percentages of 0.5, 1.0 and 2.0 by volume of concrete. The results indicated that there is a linear function between splitting tensile strength (F_splt) and volume percentage of fibers (V_f) [i.e. F_plt = A(V_f) + B, where A and B are correlation coefficients] as well as between splitting tensile strength (F_splt) and compressive strength (F_c) of plain series A concrete [i.e. F_splt = C (√F_c) + D, where C and D are correlation coefficients]. These relations can describe the development of splitting tensile strength of HSFRC containing no silica fume, 5% silica fume and 10% silica fume by weight of cement. On the other hand, although silica fume has an effect on compressive strength, volume percentage and aspect ratio of steel fibers has little effect.     

自密实再生混凝土配合比设计及力学性能试验研究

按改进的全计算法,参考CCES 02-2004《自密实混凝土设计与施工指南》和CECS 203-2006《自密实混凝土应用技术规程》,对再生粗骨料与天然碎石C40自密实混凝土配合比进行了设计及相关性能试验研究.试验结果表明：自密实再生混凝土砂率宜控制在50％～ 55％之间,浆体体积宜为0.4 m3/ m3左右;再生粗骨料能够配制出符合CECS 203-2006《自密实混凝土应用技术规程》中新拌混凝土工作性能要求的自密实再生混凝土,其28天立方体抗压强度最高达52.3 MPa,轴心抗压强度最高达36.8 MPa;自密实再生混凝土的强度、弹性模量均低于天然碎石自密实混凝土.     

结构自密实混凝土的抗压强度检测与评定研究

为了对结构自密实混凝土的抗压强度进行准确检测与评定,结合实际工程实践,采用无损检测方法和破损方法,试验研究C40和C30两种强度等级自密实混凝土的构件和同条件试件抗压强度之间的对应关系。结果表明:采用现行的回弹法对结构自密实混凝土抗压强度进行评定时,所得到的测区自密实混凝土的回弹法抗压强度换算值偏低。可通过采取修正处理的方式,按现行的《回弹法检测混凝土抗压强度技术规程》对结构自密实混凝土的抗压强度进行检测与评定。     

Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

Self-compacting concrete (SCC) flows into place and around obstructions under its own weight to fill the formwork completely and self-compact without any segregation and blocking. Elimination of the need for compaction leads to better quality concrete and substantial improvement of working conditions. This investigation aimed to show possible applicability of genetic programming (GP) to model and formulate the fresh and hardened properties of self-compacting concrete (SCC) containing pulverised fuel ash (PFA) based on experimental data. Twenty-six mixes were made with 0.38 to 0.72 water-to-binder ratio (W/B), 183–317 kg/m³ of cement content, 29–261 kg/m³ of PFA, and 0 to 1% of superplasticizer, by mass of powder. Parameters of SCC mixes modelled by genetic programming were the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength at 7, 28 and 90 days. GP is constructed of training and testing data using the experimental results obtained in this study. The results of genetic programming models are compared with experimental results and are found to be quite accurate. GP has showed a strong potential as a feasible tool for modelling the fresh properties and the compressive strength of SCC containing PFA and produced analytical prediction of these properties as a function as the mix ingredients. Results showed that the GP model thus developed is not only capable of accurately predicting the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength used in the training process, but it can also effectively predict the above properties for new mixes designed within the practical range with the variation of mix ingredients.     

Factorial design–machine learning approach for predicting incident durations

This research proposes a hybrid approach for predicting incident duration that integrates the salient features of both factorial design of experiments (DOE) and machine learning (ML). This study compares DOE with another widely used technique, forward sequential feature selection (FSFS). Moreover, to confirm the effectiveness and robustness of the proposed approach, multiple ML techniques are employed, including linear regression, decision trees, support vector machines, ensemble trees, Gaussian process regression, and artificial neural networks. The study results are validated using data from the Houston TranStar incidents archive with over 90,000 records. The accuracy of the developed predictive models is compared based on multiple techniques (i.e., no feature selection–ML, FSFS–ML, and DOE–ML). The results revealed that the significant factors affecting incident duration identified by both DOE and FSFS include the type of vehicles involved, type of lanes affected, number of vehicles involved, number of emergency responses dispatched, incident severity level, and day of the week. The comparative results of the different feature selection and modeling approaches revealed that the hybrid DOE–ML approach outperformed the other tested analysis approaches. The best-performing model under the DOE–ML approach was the SVM with cubic kernel model. It reduced the modeling time by 83.8% while increasing the prediction error by merely 0.02%, which is not significant. Therefore, the prediction accuracy could be slightly downgraded in return for a substantial reduction in the number of variables utilized, resulting in substantial savings in the modeling time and required dataset.     

Characteristics of self-consolidating concrete using two types of lightweight coarse aggregates

This paper presents the development of lightweight aggregate self-consolidating concrete (SCC) using two types of lightweight aggregates having different densities. Lightweight aggregate SCC properties have been evaluated in terms of flowability, segregation resistance and filling capacity of fresh concrete as per the standards of the Japanese Society of Civil Engineering (JSCE). The measurement of the mechanical properties of hardened lightweight aggregate SCC, including compressive strength, splitting tensile strength, elastic moduli and density, as well as its specific strength were also carried out. The characteristics of lightweight aggregate SCC at the fresh state showed that as the density of the lightweight coarse aggregate decreases, the flowability improves but the segregation resistance tends to decrease. The 28-days compressive strength of the lightweight SCC was found to be 32 MPa or higher. The relationship between the compressive strength and the splitting tensile strength was found to be similar to the expression presented by CEB-FIP, and the relationship between the compressive strength and the elastic moduli was found to be similar to the expression suggested by ACI 318-05 which takes into consideration the density of concrete. The density of the lightweight aggregate SCC decreased by up to 14% compared to that of the control SCC, and the specific strength decreased by up to 20%.     

Application of machine learning technique for predicting and evaluating chloride ingress in concrete

The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement steel. Therefore, the chloride concentration in concrete is a vital parameter for estimating the corrosion level of reinforcement steel. This research aims at predicting the chloride content in concrete using three hybrid models of gradient boosting (GB), artificial neural network (ANN), and random forest (RF) in combination with particle swarm optimization (PSO). The input variables for modeling include exposure condition, water/binder ratio (W/B), cement content, silica fume, time exposure, and depth of measurement. The results indicate that three models performed well with high accuracy of prediction (R²≥ 0.90). Among three hybrid models, the model using GB_PSO achieved the highest prediction accuracy (R² = 0.9551, RMSE = 0.0327, and MAE = 0.0181). Based on the results of sensitivity analysis using SHapley Additive exPlanation (SHAP) and partial dependence plots 1D (PDP-1D), it was found that the exposure condition and depth of measurement were the two most vital variables affecting the prediction of chloride content. When the number of different exposure conditions is larger than two, the exposure significantly impacted the chloride content of concrete because the chloride ion ingress is affected by both chemical and physical processes. This study provides an insight into the evaluation and prediction of the chloride content of concrete in the marine environment.     

混杂纤维轻骨料混凝土力学性能试验研究

针对当前高层建筑物中普通混凝土自重大,而轻骨料混凝土强度较普通混凝土低的现状,以不同玄武岩纤维体积率(Vb)、聚丙烯纤维体积率(Vp)和陶粒代取代率(Rc)为影响因素,利用正交试验法设计了9组混杂纤维轻骨料混凝土(HF-LWC),进行了抗压强度、抗折强度和劈裂抗拉强度试验,并基于试验结果建立了强度预测模型.结果表明:三...