Dataset of long-term compressive strength of concrete with manufactured sand

Ding, Xinxin; Li, Changyong; Xu, Yangyang; Li, Fenglan; Zhao, Shunbo

doi:10.1016/j.dib.2016.01.065

Cited by 7 publications

(4 citation statements)

References 4 publications

(9 reference statements)

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“…The questionnaire used in data collection was designed by the researchers and had three sections according to the major research issues addressed in this study. Similar methods and contributions can be seen in [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] . Specifically, the data presented in the article can be helpful in policy implementation and monitoring in ICT adoption and assessing the gains accruable to ICT investments, some of the articles [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] addressed similar issues.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 64%

Data on the awareness and adoption of ICT in town planning firms in Lagos state, Nigeria

et al. 2018

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This dataset covers an investigation into awareness and adoption of information and communication technology (ICT) in town planning firms in Lagos state, Nigeria. A survey of thirty (30) town planning firms in Lagos state, Nigeria was conducted. The survey was carried out between January to March 2017 by the use of questionnaires. The dataset contains responses on the factors that influence ICT usage, barriers and constraints of ICT usage. The five (5) point Likert scale was used for quantitative data analysis. The data can help identify the level of ICT usage, identify areas of concern and solutions can be proffered based on the results of the analysis.

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Section: Experimental Design Materials and Methodsmentioning

confidence: 64%

Data on the awareness and adoption of ICT in town planning firms in Lagos state, Nigeria

et al. 2018

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“…It is critical to establish a well-assembled and vast dataset with clear and explicit descriptions, insights, and statistically significant input variables if the goal is to generate powerful and resilient ML models. As a result, the GEP-tree-based algorithms used in this study were trained using a cleaned database, including 275 experimental datasets with 11 features taken from the data-in-brief [ 37 , 38 ] worked by Zhao et al [ 39 ]. The models were developed for the compressive strength ( f c ’ ) of MSC using eleven recorded attributes as inputs: compressive strength of cement ( f ce , MPa), tensile strength of cement ( f ct , MPa), curing age ( T , days), D max of crushed stone (mm), stone powder content ( SPC , %), fineness modulus of sand ( FM ), water-to-binder ratio ( w/b ), water-to-cement ratio ( w/c ), water ( W , kg/m 3 ), sand ratio ( S , %), and slump ( Slp , mm).…”

Section: Methodsmentioning

confidence: 99%

Prediction Models for Estimating Compressive Strength of Concrete Made of Manufactured Sand Using Gene Expression Programming Model

et al. 2022

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The depletion of natural resources of river sand and its availability issues as a construction material compelled the researchers to use manufactured sand. This study investigates the compressive strength of concrete made of manufactured sand as a partial replacement of normal sand. The prediction model, i.e., gene expression programming (GEP), was used to estimate the compressive strength of manufactured sand concrete (MSC). A database comprising 275 experimental results based on 11 input variables and 1 target variable was used to train and validate the developed models. For this purpose, the compressive strength of cement, tensile strength of cement, curing age, Dmax of crushed stone, stone powder content, fineness modulus of the sand, water-to-binder ratio, water-to-cement ratio, water content, sand ratio, and slump were taken as input variables. The investigation of a varying number of genetic characteristics, such as chromosomal number, head size, and gene number, resulted in the creation of 11 alternative models (M1-M11). The M5 model outperformed other created models for the training and testing stages, with values of (4.538, 3.216, 0.919) and (4.953, 3.348, 0.906), respectively, according to the results of the accuracy evaluation parameters root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The R2 and error indices values revealed that the experimental and projected findings are in extremely close agreement. The best model has 200 chromosomes, 8 head sizes, and 3 genes. The mathematical expression achieved from the GEP model revealed that six parameters, namely the compressive and tensile strength of cement, curing period, water–binder ratio, water–cement ratio, and stone powder content contributed effectively among the 11 input variables. The sensitivity analysis showed that water–cement ratio (46.22%), curing period (25.43%), and stone powder content (13.55%) were revealed as the most influential variables, in descending order. The sensitivity of the remaining variables was recorded as w/b (11.37%) > fce (2.35%) > fct (1.35%).

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“…The long-term behavior of UHPC has been actively researched, considering various variables, and several of these studies have investigated the creep and shrinkage effects [ 11 , 12 , 13 ]. In addition, studies that investigate compressive strength development characteristics based on the age of concrete can be found in some previous literature [ 14 , 15 , 16 ]. However, there are few studies on the long-term monitoring of the compressive strength of UHPC.…”

Section: Literature Reviewmentioning

confidence: 99%

The Time-Dependent Effect in Ultra High-Performance Concrete According to the Curing Methods

et al. 2022

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Ultra-high-performance concrete (UHPC) is required to develop multifunctional concrete structures such as long-span bridges. During the construction of long-span bridges, girders exhibit significant differences in age because they use different curing days in the precast process. In this study, the performances of UHPC were compared when subjected to long-term storage under various conditions after 3-day steam curing. At 365 days, the compressive strength of steam curing is 197 MPa, moist is 191 MPa, and the air is 169 MPa. Based on these differences, prediction models were proposed for long-term performances. Furthermore, the development characteristics of compressive strength, modulus of elasticity (MOE), and flexural strength until 365 days of age were analyzed under air, moist, and steam conditions. Steam curing exhibited the highest level of strength development while air curing showed the lowest. Flexural strength showed no significant difference depending on age because steel fibers were mixed with UHPC; they significantly contributed to flexural performance. The results would contribute to recognizing differences in strength between members at sites where UHPC is applied and to managing high-quality structures constructed using precast members. These research results are expected to contribute to efficient member production and process management during the construction of large structures such as super-long-span bridges.

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Dataset of long-term compressive strength of concrete with manufactured sand

Cited by 7 publications

References 4 publications

Data on the awareness and adoption of ICT in town planning firms in Lagos state, Nigeria

Data on the awareness and adoption of ICT in town planning firms in Lagos state, Nigeria

Prediction Models for Estimating Compressive Strength of Concrete Made of Manufactured Sand Using Gene Expression Programming Model

The Time-Dependent Effect in Ultra High-Performance Concrete According to the Curing Methods

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