Prediction of the compressive strength of no-slump concrete: A comparative study of regression,neural network and ANFIS models

No-slump concrete (NSC) is defined as concrete having either very low or zero slump that traditionally used for prefabrication purposes. The sensitivity of NSC to its constituents, mixture proportion, compaction, etc., enforce some difficulties in the prediction of the compressive strength. In this paper, by considering concrete constituents as input variables, several regression, neural networks (NNT) and ANFIS models are constructed, trained and tested to predict the 28-days compressive strength of no-slump concrete (28-CSNSC). Comparing the results indicate that NNT and ANFIS models are more feasible in predicting the 28-CSNSC than the proposed traditional regression models.     

Multiple linear regression,artificial neural network,and fuzzy logic prediction of 28 days compressive strength of concrete

Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out.     

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.     

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.     

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete

The compressive strength of self-compacting concrete (SCC) needs to be determined during the construction design process. This paper shows that the compressive strength of SCC (CS of SCC) can be successfully predicted from mix design and curing age by a machine learning (ML) technique named the Extreme Gradient Boosting (XGB) algorithm, including non-hybrid and hybrid models. Nine ML techniques, such as Linear regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees (DTR), Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Network using two training algorithms LBFGS and SGD (denoted as ANN_LBFGS and ANN_SGD), are also compared with the XGB model. Moreover, the hybrid models of eight ML techniques and Particle Swarm Optimization (PSO) are constructed to highlight the reliability and accuracy of SCC compressive strength prediction by the XGB_PSO hybrid model. The highest number of SCC samples available in the literature is collected for building the ML techniques. Compared with previously published works’ performance, the proposed XGB method, both hybrid and non-hybrid models, is the most reliable and robust of the examined techniques, and is more accurate than existing ML methods (R² = 0.9644, RMSE = 4.7801, and MAE = 3.4832). Therefore, the XGB model can be used as a practical tool for engineers in predicting the CS of SCC.     

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.     

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。     

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(V_b)、聚丙烯纤维体积率(V_p)和陶粒代取代率(R_c)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:三种因素对抗折强度的提升幅度大于对抗压强度和劈裂抗拉强度的提升幅度,最大增幅为45.23%;随着V_b的增加,HF-LWC的强度逐渐增大,且V_b对HF-LWC强度的影响最显著;当V_b为0.2%、V_p为0.1%、R_c为10%时,HF-LWC强度最佳;拟合得出的HF-LWC抗压强度、抗折强度和劈裂抗拉强度模型的精度较高。     

沈海高速公路辽宁段沿线桥梁混凝土碳化概率特征研究

对沈海(沈阳—海口)高速公路辽宁段沿线混凝土桥梁的碳化实测数据进行了统计分析,建立了以混凝土抗压强度和时间为主要参数的不同地区桥梁和单个桥梁混凝土碳化深度的随机过程模型。将混凝土碳化到钢筋表面的状态作为大气环境中的耐久性极限状态,分析了沿线上海湾大桥不同服役时间的耐久性失效概率。结果表明:混凝土碳化深度随混凝土抗压强度的增大而减小,但受多种复杂因素影响,混凝土碳化深度离散性很大;混凝土碳化系数计算模型的不确定性系数服从对数正态分布;海湾大桥混凝土使用到第100年时的耐久性失效概率小于10%。     

基于ANFIS的碳纤维布与混凝土黏结强度预测

基于模糊神经网络的非线性拟合能力和推理机制,研究了自适应模糊神经推理系统ANFIS在碳纤维布与混凝土的极限黏结强度预测中的应用,设计了一阶TSK模糊推理网络,建立碳纤维布厚度、宽度、黏结长度、弹性模量、抗拉强度、混凝土抗压强度、抗拉强度、宽度与极限黏结强度之间的高度非线性关系,用于黏结强度的预测。测试结果表明,自适应模糊神经推理系统计算简单、预测准确,在碳纤维布与混凝土的黏结强度预测方面优势明显。     

长径比对混杂纤维增强混凝土力学性能的影响

讨论了碳纤维(CF)和聚丙烯纤维(PF)、玻璃纤维(GF)和聚乙烯纤维(PeF)组成的混杂纤维增强混凝土的抗压强度、抗弯强度与纤维长径比之间的关系．试验结果表明，碳纤维、聚丙烯纤维、玻璃纤维和聚乙烯纤维的长径比对混凝土的抗压强度影响较小，而对抗弯强度则有明显影响．     

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.     

Data driven models for compressive strength prediction of concrete at high temperatures

The use of data driven models has been shown to be useful for simulating complex engineering processes, when the only information available consists of the data of the process. In this study, four data-driven models, namely multiple linear regression, artificial neural network, adaptive neural fuzzy inference system, and K nearest neighbor models based on collection of 207 laboratory tests, are investigated for compressive strength prediction of concrete at high temperature. In addition for each model, two different sets of input variables are examined: a complete set and a parsimonious set of involved variables. The results obtained are compared with each other and also to the equations of NIST Technical Note standard and demonstrate the suitability of using the data driven models to predict the compressive strength at high temperature. In addition, the results show employing the parsimonious set of input variables is sufficient for the data driven models to make satisfactory results.     

钢-无机纤维对轻骨料混凝土力学性能及耐久性能的影响

为了研究钢-无机纤维对轻骨料混凝土力学性能和耐久性能的影响,设计不同掺量的单掺陶瓷纤维和玄武岩纤维以及不同掺杂方式的混杂钢-玄武岩纤维和钢-陶瓷纤维增强轻骨料混凝土试件。结果表明,钢-玄武岩纤维对轻骨料混凝土抗压强度和60d透水时水压强度提高最明显,最大增幅分别达14.5%、42.9%;掺入1.35kg/m3玄武岩纤维对抗折强度增幅为62.2%;掺入陶瓷纤维降低了抗压强度和抗渗性能,但提高了抗折强度及抗冻性能;钢-陶瓷纤维对抗渗性能和抗冻性能提升效果较好。     

The maturity method: Modifications to improve estimation of concrete strength at later ages

Two modifications have been proposed for the Nurse–Saul maturity function to get better estimates of compressive strength of concrete cured at different temperatures. The modifications account for the effect of w/c ratio on the temperature dependence of strength development and the effect of curing temperature on the long-term strength. The effect of the proposed modifications on the estimation of concrete strength using the Nurse–Saul maturity function have been compared with the estimation using unmodified Nurse–Saul equation with two different datum temperatures (i.e., T₀ = −10 °C and T₀ = 0 °C). The results show that applying the proposed modifications improves the accuracy of estimated concrete strength at different curing temperatures, especially at later ages.     

混杂纤维混凝土的力学性能及抗渗性能

进行了混杂纤维(钢纤维-改性聚丙烯纤维)混凝土力学性能及抗渗性能的试验研究.结果表明,混杂纤维可以提高混凝土的抗压强度、劈拉强度和抗折强度,但对混凝土抗渗性能影响不大.引气剂有助于提高混杂纤维混凝土的抗渗性.另外,简单分析了纤维混杂方式对混凝土力学性能和抗渗性能影响的机理.     

Compressive strength, chloride diffusivity and pore structure of high performance metakaolin and silica fume concrete

This study is to relate the mechanical and durability properties of high performance metakaolin (MK) and silica fume concretes to their microstructure characteristics. The compressive strength and chloride penetrability of the control and the concretes incorporated with MK or silica fume (SF) at water-to-binder (w/b) ratios of 0.3 and 0.5 are determined. The pore size distribution and porosity of the concretes are also measured. The effect of MK and SF on the interfacial porosity is discussed based on test results. It is found that MK concrete has superior strength development and similar chloride resistance to SF concrete, and the MK concrete at a w/b of 0.3 has a lower porosity and smaller pore sizes than the control (plain) concrete. The resistance of the concretes to chloride ion penetration correlates better with the measured concrete porosity than with the paste porosity. The differences between the measured and calculated concrete porosity is smaller for MK and SF incorporated concrete than for the control concrete, indicating an improvement in the interfacial microstructure with the incorporation of the pozzolanas. This difference is found to be related to the strength and chloride penetrability of concrete to some degree.     

氯化钙及陶粒参数对泡沫混凝土性能的影响

陶粒泡沫混凝土具有良好的隔热保温性能及较高的强度。基于国内外研究现状,采用物理发泡工艺制备了掺氯化钙的陶粒泡沫混凝土,采用室内实验的方式对混凝土的力学性能影响因素进行了研究。结果表明:影响陶粒泡沫混凝土性能的主要因素为陶粒掺量、陶粒粒径、氯化钙掺量;当陶粒掺量为45%时,混凝土的28 d抗压强度最高;陶粒粒径小于10 mm时,试件吸水率和均匀性较好;氯化钙掺量为3%时,试件的抗压强度最高。     

聚丙烯-玄武岩混杂纤维对陶粒混凝土力学性能的影响

对掺加聚丙烯-玄武岩混杂纤维的陶粒混凝土进行了抗压强度、抗折强度、劈裂抗拉强度试验,得到了混杂纤维对陶粒混凝土力学性能的影响规律。结果表明:混杂纤维掺量为0.2%时,陶粒混凝土的抗压强度、劈裂抗拉强度、抗折强度提升幅度最大,分别较基准组提高了11.21%、30.73%、15.26%,但掺量过大时陶粒混凝土的力学性能会下降,甚至出现负效应;聚丙烯纤维与玄武岩纤维的混杂比为2∶1时,其对陶粒混凝土的增强效果较好;混杂纤维能增强陶粒混凝土的韧性,对抗折强度和抗拉强度提升效果明显,对抗压强度提升效果较小。     

Effect of reducing coarse aggregates on concrete strength

This paper reports the results of experimental investigation carried out on the effect of reducing coarse aggregate (CA) quantity in mix proportions on the compressive strength of concrete. It also presents empirical formulas aimed at optimizing a concrete mix design for desert regions. Intensive laboratory experiment of 1350 samples of 30 different concrete mixes using three curing methods was carried out. The influences of the water/cement (W/C) ratio, coarse and fine aggregates (FA), CA/total aggregate (CA/TA) ratio, TA/C ratio, and curing methods (air curing, oven curing, and water curing) on the compressive strength of concrete were characterized and analyzed. Mathematical formula was developed for concrete strength as a function of CA quantity that ranges from the standard quantity to null, and another formula was developed for the quantity of FA as a function of compressive strength.