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INVESTIGATION OF THE STRUCTURAL CHARACTERISTICS OF LIME-CEMENT CONCRETE
ABSTRACT
This work investigated the structural characteristics of lime cement concrete using 30 selected mix ratios. The properties studied include, compressive strength, flexural strength, splitting tensile strength, shear strength, poisson ratio, modulus of elasticity, and modulus of rigidity. A total of 360 concrete cube specimen, 360 concrete prototype beam specimen, and 360 concrete cylinder specimen were cast and cured in open water tanks. 3 specimen were cast for each mix proportion. They were then tested in compression, flexure, and splitting tension respectively at 7 days, 14 days, 21 days, and 28 days. Load values obtained from these test were used to determine the other structural properties of the concrete. Materials used in concrete production were the portland cement (PC), hydrated lime (HL), river sand, granite chippings, and water. The highest value of compressive strength recorded from experimental works at 28 days of curing was 30.83N/mm2. This occurred at a water-cement (w/c) ratio of 0.562, having a percentage replacement of PC with hydrated lime of 18.75%. Highest values of flexural strength, splitting tensile strength, shear strength, poisson ratio, modulus of elasticity and modulus of rigidity recorded at 28 days of curing were 5.03N/mm2, 3.725N/mm2, 1.257N/mm2, 0.216, 30.708 x 103 N/mm2 and 13.386 x 103 N/mm2 respectively. Lowest values recorded for compressive strength, flexural strength, split tensile strength, shear strength, poisson ratio, modulus of elasticity and modulus of rigidity recorded at 28 days of curing were 15.12N/mm2, 2.28N/mm2, 2.00N/mm2, 0.569N/mm2, 0.105, 20.264 x 103N/mm2 and 8.803 x 103N/mm2 respectively. A total of 120 data set were generated experimentally for each property studied. 114 sample data of each property were used to teach the artificial neural networks (ANNs) how to accurately predict the structural properties of the lime cement concrete. The remaining 6 sets of data were left out and used to test how well the networks were predicting after being trained. 7 ANN models were created using the neural network toolbox in the Matlab R2014a software. The feed forward back propagation neural network with “trainlm” training function and the mean square error (mse) performance functions were adopted. The end results of the back propagation neural networks were 6-20-1 (6 inputs, 20 neurons in the hidden layer, and 1 output). Maximum percentage error for all networks were generally below 11% while the maximum correlation coefficients were close to 1. The student’s ttest was used to further test the adequacy of the neural network models. The calculated T values for the compressive strength, flexural strength, split tensile strength, shear strength, poisson ratio, modulus of elasticity, and modulus of rigidity neural networks were 1.437, 0.1598, 0.4607, 1.4642, -1.0555, 0.4631, and 1.7069 respectively. They were all less than the 2.065 which is the allowable T value from the statistical table. Therefore, the null hypothesis (Ho) was accepted i.e. there is no significant difference between the neural network models and the experimental results. For lime cement concrete to be used as a structural concrete, PC replacement with hydrated lime must not be up to 30%. Optimum percentage replacement was recorded at 18.75%. Partial replacement of portland cement with hydrated lime was observed to improve the workability of the fresh concrete but reduced the strength of the hardened concrete. The relationship between the structural properties of the lime cement concrete with respect to water cement ratio, showed that the magnitude of each property of concrete increased as water cement ratio increased until the optimum water cement ratios were reached. With the use of the developed ANN models, mix design procedures for lime cement concrete can be carried out with lesser time, and energy requirement since the traditional method of designing mixes by carrying out trial mixes in the laboratory will no longer be required.
Keywords: Structural properties, concrete, hydrated lime (HL), portland cement (PC), artificial neural network (ANN).
TABLE OF CONTENTS
Title page i
Certification ii
Dedication iii
Acknowledgements iv
Abstract v
Table of contents vi
List of tables xi
List of figures xv
List of plates xviii
CHAPTER ONE: INTRODUCTION 1
1.1 Background 1
1.2 Statement of problem 4
1.3 Objectives of the study 5
1.4 Justification of study 5
1.5 Scope 6
CHAPTER TWO: LITERATURE REVIEW 8
2.1 Cementing materials 8
2.1.1 Portland cement 8
2.1.1.1 Chemical composition of portland cement 9
2.1.1.2 Types of portland cement 10
2.1.1.3 Properties of cement compounds 14
2.1.2 Lime 16
2.1.2.1 Types of lime 17
2.1.2.2 Properties of hydrated lime 19
2.1.2.3 Comparison of the chemical properties of Portland cement and hydrated lime 20
2.1.2.4 Lime cycle 21
2.1.2.5 Uses of lime in the construction industry 22
2.1.2.6 Calcination temperature of limestone 23
2.1.2.7 Occurrence of limestone in Nigeria 24
2.1.3 Other cementing materials – pozzolans 26
2.1.3.1 Advantages of pozzolan in the building industry 28
2.2 Aggregates 30
2.2.1 Classification of aggregates according to size 31
2.2.2 Classification of aggregates based on bulk density 32
2.2.3 Sieve analysis of aggregates 33
2.2.3.1 Fineness modulus of aggregates 36
2.3 Concrete 36
2.3.1 Types of concrete 37
2.3.1.1 Cement concrete 37
2.3.1.2 Lime concrete 38
2.3.1.3 Lime cement concrete 40
2.3.1.4 Laterized concrete 47
2.3.2 Properties of concrete 48
2.3.2.1 Compressive strength 48
2.3.2.2 Flexural strength 50
2.3.2.3 Splitting tensile strength 53
2.3.2.4 Shear strength 55
2.3.2.5 Poisson ratio 56
2.3.2.6 Static modulus of elasticity 57
2.3.2.7 Modulus of rigidity 60
2.3.3 Workability of fresh concrete 61
2.3.3.1 Slump test of fresh concrete 62
2.3.4 Density of hardened concrete 62
2.3.5 Concrete grade 63
2.4 Artificial neural network (ANN) 64
2.4.1 The human brain 66
2.4.2 The artificial neuron 68
2.4.3 Network architecture 69
2.4.4 Learning process of the neural network 72
2.4.5 Activation function 75
2.4.6 Standard back propagation neural network 79
2.4.6.1 Feed-forward computation 81
2.4.6.2 Back propagation of error using the Levenberg-Marquardt algorithm 83
2.4.7 Damping parameter 87
2.4.8 Initialization 87
2.4.9 Local minima 88
2.4.10 Normalizing inputs 88 2.4.11 Pattern presentation 88
2.4.12 Benefits of using the neural network 89
2.4.13 Application of neural network 92
2.5 Graphical user interface 93
CHAPTER THREE: MATERIALS AND METHODS 94
3.1 Materials 94
3.2 Methods 95
3.2.1 Characterization of the fresh lime cement concrete and its constituent
using experimental methods 96
3.2.1.1 Sieve analysis to determine the grain size distribution of aggregates 96
3.2.1.2 Bulk density test of aggregates 97
3.2.1.3 Slump test on concrete 99
3.2.1.4 Initial and final setting time test for the cement and lime paste. 100
3.2.1.5 Chemical property test for the hydrated lime 101
3.2.2 Structural characteristics test on the hardened lime cement concrete using experimental methods 101
3.2.2.1 Compressive strength test 101
3.2.2.2 Flexural strength test 104
3.2.2.3 Splitting tensile strength test 107
3.2.2.4 Shear strength 109
3.2.2.5 Poisson’s ratio 109
3.2.2.6 Static modulus of elasticity 110
3.2.2.7 Shear modulus/ modulus of rigidity 110
3.2.3 Formulation and validation of artificial neural network models
using the prediction method 111
3.2.3.1 Workflow for the design of the neural network process 114
3.2.4 Adequacy of network predictions using statistical methods 118
| 3.2.5 Graphical user interface (GUI) for predicting properties of lime cement | ||
| concrete
|
119 | |
| CHAPTER FOUR: RESULTS AND DISCUSSION | 120 | |
| 4.1 Results | 120 | |
| 4.1.1 Properties of fresh lime cement concrete and its constituents | 120 | |
| 4.1.1.1 Sieve analysis of river sand and granite chippings | 120 | |
| 4.1.1.2 Bulk density of aggregates | 122 | |
| 4.1.1.3 Workability test on concrete mixes | 123 | |
| 4.1.1.4 Setting time test | 124 | |
| 4.1.1.5 Chemical property test for the hydrated lime and the portland cement | 125 | |
| 4.1.2 Structural characteristics test results on the hardened lime cement concrete | 126 | |
| 4.1.3 Formulation of the artificial neural network models | 137 | |
| 4.1.3.1 Selection of training data | 139 | |
| 4.1.4 Validation of network performance | 139 | |
| 4.1.5 Test of adequacy of the neural network predictions with the | ||
| experimental values | 161 | |
| 4.1.6 Comparison of predicted and experimental values of structural | ||
| characteristics of lime cement concrete | 167 | |
| 4.1.7 Graphical user interface (GUI) for predicting the structural properties | ||
| of lime cement concrete | 178 | |
| 4.2 Discussion | 179 | |
| 4.2.1 Characterization of the fresh lime cement concrete and its constituent | 179 | |
| 4.2.1.1 Sieve analysis of river sand and granite chippings | 179 | |
| 4.2.1.2 Bulk densities for river sand and granite chippings | 180 | |
| 4.2.1.3 Workability test of the fresh lime cement concrete | 180 | |
| 4.2.1.4 Setting time test results for hydrated lime paste and portland cement paste | 181 | |
| 4.2.1.5 Chemical analysis test results for hydrated lime and portland cement | 181 | |
| 4.2.2 Structural characteristics test results on the hardened lime cement concrete | 182 | |
| 4.2.3 Performance validation of artificial neural network (ANN) models | ||
| developed | 186 | |
| 4.2.4 Test of adequacy of the neural network models | 190 | |
4.2.5 Comparison of results of experimental values with artificial neural
network (ANN) predictions 190
4.2.6 Graphical User Interface developed 191
4.2.7 Effects of partial replacement of portland cement with hydrated lime in concrete production 191
4.2.8 Effect of water cement ratio on the structural properties of lime cement concrete 192
CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS 194
5.1 Conclusion 194
5.2 Recommendations 196
5.3 Contribution to knowledge 196
References
Appendices
CHAPTER ONE
INTRODUCTION
1.1 Background
Concrete is one of the most important materials in modern building and civil engineering constructions. Today, its versatility in terms of its workability (i.e. its ability to be moulded into various shapes required), makes it very unique (Ajayi, Rasheed, & Mojirade, 2013). Concrete can be defined as a composite material comprising mainly of three phases, namely, coarse aggregate, cement mortar and the interface zone between them (Tareq, 2008).
The characteristics of the interface zone, largely govern the bond between cement paste or mortar and aggregate. But Jaime (2013), defined concrete as a mixture of binders, aggregates and water. Binders are classified into inorganic and organic binders. Examples of inorganic binders are cement, lime and gypsum, while the organic binders are epoxy resin and acrylic emulsion. There are also supplementary binders which are the pulverized fly ash (PFA) and ground granulated blast furnace slag (GGBS).
Conventional concrete, is a composite material containing fine aggregate, coarse aggregate, cement and water in predefined mix proportions. This combination gives concrete its characteristic density, which is normally within the range of 2200kg/m3 – 2400kg/m3, thereby limiting its use in some structural works (Osunade, 2002).Concrete is used in large quantities, almost everywhere mankind has a need for infrastructure. It is probably the most common material used in the construction industry of most countries of the world (Bhavikatti, 2001).It is strong in compression and has good fire resistance properties. When steel, which is strong in tension, is incorporated into it, a strong and durable material which can withstand various forms of loading and can be formed into various shapes and sizes emerges (Owolabi, 2012). This accounts for its widespread use in civil engineering construction works such as buildings, bridges, dams, etc.
The manufacture and use of concrete lead to a wide range of environment and social consequences. Cement, which is a major component of concrete, exerts similar environmental and social effects (Navdeep, Sudhakara, & Abhijit, 2012). Cement production is a significant source of global carbon dioxide (CO2) emissions. This gas depletes the ozone layer, i.e. the “greenhouse effect” that has caused a lot of harm to the ecosystem by increasing the atmospheric temperature (Srinivasan, Sathiya, & Palanisamy, 2010). The cement industry is one of the three primary producers of carbon dioxide (CO2). The other two are the energy production and transportation industries. The common estimation of the emission of CO2 from the cement production, is given as 0.6 ton for every 1 ton of cement (Jaime, 2013). In China, the statistics in 2005 for the CO2 emission from cement production was 0.815tonne of CO2 per ton of cement. As at 2011, cement production contributed 7% to global anthropogenic CO2 emission; largely due to the sintering of limestone and clay at 15000C (Navdeep et al., 2012). The increasing atmospheric temperature due to the emission of CO2 gas, has led to climatic changes with adverse effects such as flooding, earthquakes, hurricanes, new viruses, etc.
Most cement plants consume much energy and produce a large amount of undesirable products, which affect the environment (Ahmed, Abdurrahman, & Mohammed, 2009). According to
Cowper (2013), the manufacture of cement consumes a large amount of energy, which is about 7,600,000KJ per ton or 1.1 ton of cement. Production of cement requires large quantities of energy and developing countries like Nigeria, have low availability of non-renewable energy resources to take care of this need.
Cement production is relatively expensive due to high cost of energy. Thermal and electric energy, account for 40% of the operational costs (“European Commission”, 2010). This problem discourages an average potential investor from venturing into cement manufacture. The result is that only a few investors monopolize the market in Nigeria.
Owning a house is one of the most cherished desire of an average Nigerian. Unfortunately, many middle class and low class earners, are not able to buy or build houses of their own because of the high cost of building materials, especially cement. Fifty percent (50%) of the total cost of any construction project goes on cement (Okpala, 1988).In Nigeria, the demand for cement exceeds its supply. Existing cement factories, have their total installed capacity equal to 50% of the country’s requirement, while the actual production from these factories, is less than 30% of the country’s demand (Apata & Alhassan, 2012).
Concrete is designed by past experience acquired from previous mixes or by making trial batches in the laboratory and testing the concrete. Results obtained from the laboratory test, usually, require some modification to meet with the site requirement. All these traditional procedures are expensive and time consuming, making mix design more difficult and complicated (Shetty, 2006).
The provision of building materials that are affordable to urban and rural dwellers, as well as environmentally friendly, has been seen to be one of the hindrances to improved housing situations in developing countries like Nigeria (Jimoh et al., 2013). Some of the conventional materials are imported and their prices are beyond what the average Nigerian can afford. In order to check the over dependence on these materials, efforts are being directed towards changing some of the materials, such as concrete by wholly or partially substituting their constituents. In the recent years, the use of binding materials of different types, together with cement, has become very wide in the production of concrete. Example of this, can be seen in the blending of portland cement with fly ash, limestone, rice husk ash, pawpaw leaf ash, plantain leaf ash, corn cob ash, hypo sludge, saw dust ash, palm bunch ash, etc. Ternary blended cement, which has the advantage of increase in strength at longer days of hydration, when compared to their controls and binary blends, had been investigated by Ettu, Nwachukwu, Arimanwa, Awodiji, & Opara (2013a).
In Nigeria, there has been reawakened serious awareness on the need to relate research to production, especially in the use of local materials as alternatives for the construction of functional, but low-cost dwelling, both in the urban and rural areas (Joshua and Lawal, 2011). One of such local material that is being researched on is limestone. Therefore, this research work is concerned with the investigation of the properties of concrete in which cement has been partially replaced by hydrated lime as a binder. Besides, models based on the artificial neural network, were also developed for predicting these properties of lime cement concrete.
1.2 Statement of problem
Concrete has been the most widely used construction material for many centuries due to its advantages such as ease in forming structural elements, readily available, and excellent durability relative to other materials (Jayakumar and Abdullahi, 2011). But the increase in the demand of concrete for construction works, has resulted to an increase in the demand for the production of cement, which is a significant source of global carbon dioxide (CO2) emission. This green-house gas is depleting the ozone layer and thereby causing global warming of the earth.
The large amount of energy required for portland cement production has resulted to high production cost. This has led to the monopolization of the cement industry by few investors who can afford the very high production cost of the cement. The high production cost of cement has also resulted to high cost of the product itself, thereby making it difficult for low and average income earners to own houses. Finally, the traditional method of mix design of concrete is time consuming, and energy demanding. Therefore, with the use of hydrated lime as a partial replacement of portland cement in concrete production and with the use of the developed artificial neural network models for predicting the properties of lime cement concrete mixes, these problems can be minimized.
1.3 Objectives of the study
The main objective of this study, is to investigate the structural characteristics of lime-cement concrete. The specific objectives are as follows:
- To characterize the fresh lime cement concrete and its constituent.
- To determine experimentally, the structural characteristics of hardened lime-cement concrete. These characteristics include compressive strength, flexural strength, split tensile strength, shear strength, poisson ratio, modulus of elasticity, and modulus of rigidity.
- To formulate, validate, and test the adequacy of the artificial neural network (ANN) models for predicting the structural characteristics of lime-cement concrete.
- To compare the predicted results of the structural characteristics of lime cement concrete obtained from the artificial neural network models, and the experimental values.
- To prepare a user interface for the artificial neural network models.
1.4 Justification of study
This investigation will result to the provision of data on the structural characteristics of limecement concrete. This will assist in providing information to structural designers, in the analysis and design of hydrated lime cement concrete structures, since there are no available standard design codes with respect to this type of concrete.
The inclusion of hydrated lime as a partial replacement of portland cement, will assist in reducing the emission of the green-house gases to the atmosphere. This is possible since a reduction in the amount of the clinker content in cement production by hydrated lime, will reduce the amount of CO2 released into the atmosphere during the calcination of the clinker (Afsah, 2004). Also, the addition of hydrated lime as a partial replacement of clinker, will result to lower calcination temperature, thereby reducing CO2 emissions from the fossil fuel used to heat up the cement kilns.
Hydrated lime in concrete has the ability to re-absorb CO2 gases from the atmosphere (Spano, 2009).Therefore, since lime production leaves a smaller carbon footprint than OPC, the use of lime cement concrete, will lead to a reduction of green-house gases to the atmosphere.
Lime production requires lower energy consumption when compared to portland cement. The vertical kiln used for the production of lime, tends to operate at temperatures between 8000C and 10000C. This is substantially lower than the 14500C, which is needed for the calcination of limestone to produce portland cement (Yang, 2013). Also, quicklime tends to disintegrate during slaking, substantially reducing the demand for the energy-intensive finish grinding.
The production of hydrated lime, requires far less imported technology and equipment. The energy requirement is also lower than that of portland cement. These factors result to the lower production cost of hydrated lime. Lower production cost results into lower prices of lime cement for consumers, thereby leading to affordable housing units. More investors can now venture into cement manufacturing, since the initial investment cost is reduced, thereby generating opportunities for local employment.
The use of the formulated models to predict the structural characteristics of lime-cement concrete, is expected to reduce the labour involved in the mix design process and save time. This will be achieved since the designer do not have to waste materials, energy and time, making trial mixes, curing them, and crushing them in the laboratory. Artificial neural networks have the ability to make accurate predictions even when input data is incomplete or non-linear. Predictions can be easily made for any given number of mixtures as against the use of other mathematical models.
1.5 Scope of study
The scope of this study is limited to determining the structural characteristics of lime-cement concrete. The structural characteristics studied are compressive strength, flexural strength, split tensile strength, shear strength, poisson ratio, modulus of elasticity, and modulus of rigidity. These properties were obtained from experimental works and then, the artificial neural network (ANN) technique was used to develop models for predicting them. The ANN toolbox in the matlab R2014a software was adopted in the development of the ANN models.
INVESTIGATION OF THE STRUCTURAL CHARACTERISTICS OF LIME-CEMENT CONCRETE
