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The ITZ microstructure, thickness, porosity and its relation with compressive and flexural strength of

《结构与土木工程前沿（英文）》 2022年 第16卷 第2期   页码 191-201 doi: 10.1007/s11709-021-0792-y

摘要： A new insight into the interfacial transition zone (ITZ) in cement mortar specimens (CMSs) that is influenced by cement fineness is reported. The importance of cement fineness in ITZ characterizations such as morphology and thickness is elucidated by backscattered electron images and by consequences to the compressive (Fc) and flexural strength (Ff), and porosity at various water/cement ratios. The findings indicate that by increasing the cement fineness the calcium silicate hydrate formation in the ITZ is favored and that this can refine the pore structures and create a denser and more homogeneous microstructure. By increasing cement fineness by about 25% of, the ITZ thickness of CMSs was reduced by about 30% and Fc was increased by 7%–52% and Ff by 19%–40%. These findings illustrate that the influence of ITZ features on the mechanical strength of CMSs is mostly related to the cement fineness and ITZ microstructure.

关键词： cement fineness     interfacial transition zone     compressive and flexural strength    

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Optimizing the compressive strength of concrete containing micro-silica, nano-silica, and polypropylene

Fatemeh ZAHIRI, Hamid ESKANDARI-NADDAF

《结构与土木工程前沿（英文）》 2019年 第13卷 第4期   页码 821-830 doi: 10.1007/s11709-019-0518-6

摘要： Many studies have evaluated the effects of additives such as nano-silica (NS), micro-silica (MS) and polymer fibers on optimizing the mechanical properties of concrete, such as compressive strength. Nowadays, with progress in cement industry provides, it has become possible to produce cement type I with strength classes of 32.5, 42.5, and 52.5 MPa. On the one hand, the microstructure of cement has changed, and modified by NS, MS, and polymers; therefore it is very important to determine the optimal percentage of each additives for those CSCs. In this study, 12 mix designs containing different percentages of MS, NS, and polymer fibers in three cement strength classes(CSCs) (32.5, 42.5, and 52.5 MPa) were designed and constructed based on the mixture method. Results indicated the sensitivity of each CSCs can be different on the NS or MS in compressive strength of concrete. Consequently, strength classes have a significant effect on the amount of MS and NS in mix design of concrete. While, polymer fibers don’t have significant effect in compressive strength considering CSCs.

关键词： mixture method     compressive strength     nano-silica     micro-silica     polypropylene fibers    

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

Mahmood AKBARI, Vahid JAFARI DELIGANI

《结构与土木工程前沿（英文）》 2020年 第14卷 第2期   页码 311-321 doi: 10.1007/s11709-019-0593-8

摘要： 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 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.

关键词： data driven model     compressive strength     concrete     high temperature    

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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

《结构与土木工程前沿（英文）》 2021年 第15卷 第2期   页码 520-536 doi: 10.1007/s11709-021-0689-9

摘要： This study aims to improve the unconfined compressive strength of soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based models. The soils used in this study are stabilized using various combinations of cement, lime, and rice husk ash. To predict the results of unconfined compressive strength tests conducted on soils, a comprehensive laboratory dataset comprising 137 soil specimens treated with different combinations of cement, lime, and rice husk ash is used. Two artificial-intelligence-based models including artificial neural networks and support vector machines are used comparatively to predict the strength characteristics of soils treated with cement, lime, and rice husk ash under different conditions. The suggested models predicted the unconfined compressive strength of soils accurately and can be introduced as reliable predictive models in geotechnical engineering. This study demonstrates the better performance of support vector machines in predicting the strength of the investigated soils compared with artificial neural networks. The type of kernel function used in support vector machine models contributed positively to the performance of the proposed models. Moreover, based on sensitivity analysis results, it is discovered that cement and lime contents impose more prominent effects on the unconfined compressive strength values of the investigated soils compared with the other parameters.

关键词： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

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

《结构与土木工程前沿（英文）》 2023年 第17卷 第2期   页码 284-305 doi: 10.1007/s11709-022-0901-6

摘要： 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.

关键词： compressive strength     self-compacting concrete     artificial neural network     decision tree     CatBoost    

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Effect of calcium lactate on compressive strength and self-healing of cracks in microbial concrete

Kunamineni VIJAY, Meena MURMU

《结构与土木工程前沿（英文）》 2019年 第13卷 第3期   页码 515-525 doi: 10.1007/s11709-018-0494-2

摘要： This paper presents the effect on compressive strength and self-healing capability of bacterial concrete with the addition of calcium lactate. Compared to normal concrete, bacterial concrete possesses higher durability and engineering concrete properties. The production of calcium carbonate in bacterial concrete is limited to the calcium content in cement. Hence calcium lactate is externally added to be an additional source of calcium in the concrete. The influence of this addition on compressive strength, self-healing capability of cracks is highlighted in this study. The bacterium used in the study is and was added to both spore powder form and culture form to the concrete. spore powder of 2 million cfu/g concentration with 0.5% cement was mixed to concrete. Calcium lactates with concentrations of 0.5%, 1.0%, 1.5%, 2.0%, and 2.5% of cement, was added to the concrete mixes to test the effect on properties of concrete. In other samples, cultured with a concentration of 1×10 cells/mL was mixed with concrete, to study the effect of bacteria in the cultured form on the properties of concrete. Cubes of 100 mm×100 mm×100 mm were used for the study. These cubes were tested after a curing period of 7, 14 and 28 d. A maximum of 12% increase in compressive strength was observed with the addition of 0.5% of calcium lactate in concrete. Scanning electron microscope and energy dispersive X-ray spectroscopy examination showed the formation of ettringite in pores; calcium silicate hydrates and calcite which made the concrete denser. A statistical technique was applied to analyze the experimental data of the compressive strengths of cementations materials. Response surface methodology was adopted for optimizing the experimental data. The regression equation was yielded by the application of response surface methodology relating response variables to input parameters. This method aids in predicting the experimental results accurately with an acceptable range of error. Findings of this investigation indicated the influence of added calcium lactate in bio-concrete which is quite impressive for improving the compressive strength and self-healing properties of concrete.

关键词： calcium lactate     bacillus subtilis     compressive strength     self-healing of cracks    

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Enhancing compressive strength and durability of self-compacting concrete modified with controlled-burnt

《结构与土木工程前沿（英文）》 2022年 第16卷 第2期   页码 161-174 doi: 10.1007/s11709-021-0796-7

摘要： In sugar industries, the growing amount of sugarcane bagasse ash (SBA), a byproduct released after burning bagasse for producing electricity, is currently causing environmental pollution. The residual ash displays a pozzolanic potential; and hence, it has potential as a cement addictive. This study focuses on enhancing suitability of SBA through incorporating ground blast furnace slag (BFS) in manufacturing self-compacting concretes (SCCs). For this purpose, SBA was processed by burning at 700 °C for 1 h, before being ground to the cement fineness of 4010 cm2/g. SCC mixtures were prepared by changing the proportions of SBA and BFS (i.e., 10%, 20%, and 30%) in blended systems; and their performance was investigated. Test results showed that the presence of amorphous silica was detected for the processed SBA, revealing that the strength activity index was above 80%. The compressive strength of SCC containing SBA (without BFS) could reach 98%−127% of that of the control; combination of SBA and 30% BFS gets a similar strength to the control after 28 d. Regarding durability, the 10%SBA + 30%BFS mix exhibited the lowest risk of corrosion. Moreover, the joint use of SBA and BFS enhanced significantly the SCC’s sulfate resistance. Finally, a hyperbolic formula for interpolating the compressive strength of the SBA-based SCC was proposed and validated with error range estimated within ±10%.

关键词： sugarcane bagasse ash     self-compacting concrete     compressive strength     sulfate resistance     water absorption     strength formula    

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Influence of accelerated curing on the compressive strength of polymer-modified concrete

Izhar AHMAD; Kashif Ali KHAN; Tahir AHMAD; Muhammad ALAM; Muhammad Tariq BASHIR

《结构与土木工程前沿（英文）》 2022年 第16卷 第5期   页码 589-599 doi: 10.1007/s11709-022-0789-1

摘要： In recent building practice, rapid construction is one of the principal requisites. Furthermore, in designing concrete structures, compressive strength is the most significant of all parameters. While 3-d and 7-d compressive strength reflects the strengths at early phases, the ultimate strength is paramount. An effort has been made in this study to develop mathematical models for predicting compressive strength of concrete incorporating ethylene vinyl acetate (EVA) at the later phases. Kolmogorov-Smirnov (KS) goodness-of-fit test was used to examine distribution of the data. The compressive strength of EVA-modified concrete was studied by incorporating various concentrations of EVA as an admixture and by testing at ages of 28, 56, 90, 120, 210, and 365 d. An accelerated compressive strength at 3.5 hours was considered as a reference strength on the basis of which all the specified strengths were predicted by means of linear regression fit. Based on the results of KS goodness-of-fit test, it was concluded that KS test statistics value (D) in each case was lower than the critical value 0.521 for a significance level of 0.05, which demonstrated that the data was normally distributed. Based on the results of compressive strength test, it was concluded that the strength of EVA-modified specimens increased at all ages and the optimum dosage of EVA was achieved at 16% concentration. Furthermore, it was concluded that predicted compressive strength values lies within a 6% difference from the actual strength values for all the mixes, which indicates the practicability of the regression equations. This research work may help in understanding the role of EVA as a viable material in polymer-based cement composites.

关键词： compressive strength prediction     polymer-modified concrete     linear regression fit     early age strength     ethylene vinyl acetate    

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Modeling of unconfined compressive strength of soil-RAP blend stabilized with Portland cement using multivariate

Ali Reza GHANIZADEH, Morteza RAHROVAN

《结构与土木工程前沿（英文）》 2019年 第13卷 第4期   页码 787-799 doi: 10.1007/s11709-019-0516-8

摘要： The recycled layer in full-depth reclamation (FDR) method is a mixture of coarse aggregates and reclaimed asphalt pavement (RAP) which is stabilized by a stabilizer agent. For design and quality control of the final product in FDR method, the unconfined compressive strength of stabilized material should be known. This paper aims to develop a mathematical model for predicting the unconfined compressive strength (UCS) of soil-RAP blend stabilized with Portland cement based on multivariate adaptive regression spline (MARS). To this end, two different aggregate materials were mixed with different percentages of RAP and then stabilized by different percentages of Portland cement. For training and testing of MARS model, total of 64 experimental UCS data were employed. Predictors or independent variables in the developed model are percentage of RAP, percentage of cement, optimum moisture content, percent passing of #200 sieve, and curing time. The results demonstrate that MARS has a great ability for prediction of the UCS in case of soil-RAP blend stabilized with Portland cement ( is more than 0.97). Sensitivity analysis of the proposed model showed that the cement, optimum moisture content, and percent passing of #200 sieve are the most influential parameters on the UCS of FDR layer.

关键词： full-depth reclamation     soil-reclaimed asphalt pavement blend     Portland cement     unconfined compressive strength     multivariate adaptive regression spline    

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

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

《结构与土木工程前沿（英文）》 2017年 第11卷 第1期   页码 90-99 doi: 10.1007/s11709-016-0363-9

摘要： 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.

关键词： concrete     28 days compressive strength     multiple linear regression     artificial neural network     ANFIS     sensitivity analysis (SA)    

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Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

《结构与土木工程前沿（英文）》 2019年 第13卷 第1期   页码 215-239 doi: 10.1007/s11709-018-0489-z

摘要： Plastic concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the compressive strength of bentonite/sepiolite plastic concretes. For this purpose, two unique sets of 72 data for compressive strength of bentonite and sepiolite plastic concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the compressive strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic concrete compressive strength values was evaluated by conducting parametric studies.

关键词： bentonite/sepiolite plastic concrete     compressive strength     artificial neural network     support vector machine     parametric analysis    

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Compressive behavior and microstructure of concrete mixed with natural seawater and sea sand

《结构与土木工程前沿（英文）》 2021年 第15卷 第6期   页码 1347-1357 doi: 10.1007/s11709-021-0780-2

摘要： Noncorrosive reinforcement materials facilitate producing structural concrete with seawater and sea sand. This study investigated the properties of seawater and sea sand concrete (SSC), considering the curing age (3, 7, 14, 21, 28, 60, and 150 d) and strength grade (C30, C40, and C60). The compressive behavior of SSC was obtained by compressive tests and digital image correction (DIC) technique. Scanning electron microscope (SEM) and X-ray powder diffraction (XRD) methods were applied to understand the microstructure and hydration products of cement in SSC. Results revealed a 30% decrease in compressive strength for C30 and C40 SSC from 60 to 150 d, and a less than 5% decrease for C60 from 28 to 150 d. DIC results revealed significant cracking and crushing from 80% to 100% of compressive strength. SEM images showed a more compact microstructure in higher strength SSC. XRD patterns identified Friedel’s salt phase due to the chlorides brought by seawater and sea sand. The findings in this study can provide more insights into the microstructure of SSC along with its short- and long-term compressive behavior.

关键词： seawater and sea sand concrete     compressive strength     strain field     microstructure     hydration products    

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

Van Quan TRAN; Hai-Van Thi MAI; Thuy-Anh NGUYEN; Hai-Bang LY

《结构与土木工程前沿（英文）》 2022年 第16卷 第7期   页码 928-945 doi: 10.1007/s11709-022-0837-x

摘要： 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.

关键词： compressive strength     self-compacting concrete     machine learning techniques     particle swarm optimization     extreme gradient boosting    

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

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

《结构与土木工程前沿（英文）》 2022年 第16卷 第10期   页码 1213-1232 doi: 10.1007/s11709-022-0880-7

摘要： 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.

关键词： FRCM     deep neural networks     confinement effect     strength model     confined concrete    

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Finite element analysis of controlled low strength materials

Vahid ALIZADEH

《结构与土木工程前沿（英文）》 2019年 第13卷 第5期   页码 1243-1250 doi: 10.1007/s11709-019-0553-3

摘要： Controlled low strength materials (CLSM) are flowable and self-compacting construction materials that have been used in a wide variety of applications. This paper describes the numerical modeling of CLSM fills with finite element method under compression loading and the bond performance of CLSM and steel rebar under pullout loading. The study was conducted using a plastic-damage model which captures the material behavior using both classical theory of elasto-plasticity and continuum damage mechanics. The capability of the finite element approach for the analysis of CLSM fills was assessed by a comparison with the experimental results from a laboratory compression test on CLSM cylinders and pullout tests. The analysis shows that the behavior of a CLSM fill while subject to a failure compression load or pullout tension load can be simulated in a reasonably accurate manner.

关键词： CLSM     finite element method     compressive strength     pullout     numerical modeling     plastic damage model    

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标题 作者 时间 类型 操作

The ITZ microstructure, thickness, porosity and its relation with compressive and flexural strength of

期刊论文

Optimizing the compressive strength of concrete containing micro-silica, nano-silica, and polypropylene

Fatemeh ZAHIRI, Hamid ESKANDARI-NADDAF

期刊论文

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

Mahmood AKBARI, Vahid JAFARI DELIGANI

期刊论文

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

期刊论文

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

期刊论文

Effect of calcium lactate on compressive strength and self-healing of cracks in microbial concrete

Kunamineni VIJAY, Meena MURMU

期刊论文

Enhancing compressive strength and durability of self-compacting concrete modified with controlled-burnt

期刊论文

Influence of accelerated curing on the compressive strength of polymer-modified concrete

Izhar AHMAD; Kashif Ali KHAN; Tahir AHMAD; Muhammad ALAM; Muhammad Tariq BASHIR

期刊论文

Modeling of unconfined compressive strength of soil-RAP blend stabilized with Portland cement using multivariate

Ali Reza GHANIZADEH, Morteza RAHROVAN

期刊论文

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

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

期刊论文

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

期刊论文

Compressive behavior and microstructure of concrete mixed with natural seawater and sea sand

期刊论文

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

Van Quan TRAN; Hai-Van Thi MAI; Thuy-Anh NGUYEN; Hai-Bang LY

期刊论文

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

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

期刊论文

Finite element analysis of controlled low strength materials

Vahid ALIZADEH

期刊论文