Strengthening an in-service reinforcement concrete bridge with prestressed CFRP bar

Carbon fiber reinforced polymer (CFRP) bars were prestressed for the structural strengthening of 8 T-shaped reinforced concrete (RC) beams of a 21-year-old bridge in China. The ultimate bearing capacity of the existing bridge after retrofit was discussed on the basis of concrete structures theory. The flexural strengths of RC beams strengthened with CFRP bars were controlled by the failure of concrete in compression and a prestressing method was applied in the retrofit. The field construction processes of strengthening with CFRP bars—including grouting cracks, cutting groove, grouting epoxy and embedding CFRP bars, surface treating, banding with the U-type CFRP sheets, releasing external prestressed steel tendons—were introduced in detail. In order to evaluate the effectiveness of this strengthening method, field tests using vehicles as live load were applied before and after the retrofit. The test results of deflection and concrete strain of the T-shaped beams with and without strengthening show that the capacity of the repaired bridge, including the bending strength and stiffness, is enhanced. The measurements of crack width also indicate that this strengthening method can enhance the durability of bridges. Therefore, the proposed strengthening technology is feasible and effective.

Analysis of compressive membrane action in concrete slabs

This paper summarises the results obtained from non-linear finite-element analysis (NLFEA) of a series of reinforced-concrete one-way slabs with various boundary conditions representative of a bridge deck slab strip in which compressive membrane action governs the structural behaviour. The application of NLFEA for the optimum analysis and design of in-plane restrained concrete slabs is explored. An accurate material model and various equation solution methods were assessed to find a suitable finite-element method for the analysis of concrete slabs in which arching action occurs. Finally, the results from the NLFEA are compared and validated with those from various experimental test data. Significantly, the numerical analysis was able to model the arching action that occurred as a result of external in-plane restraint at the supports and which enhanced the ultimate strength of the slab. The NLFEA gave excellent predictions for the ultimate load-carrying capacity and far more accurate predictions than those obtained using standard flexural or elastic theory.

Prediction of Fresh and Hardened Properties of Self-Consolidating Concrete Using Neurofuzzy Approach

Self-consolidating concrete (SCC) developed in Japan in the late 80s has enabled the construction industry to reduce demand on the resources, improve the work conditions and also reduce the impact on the environment by elimination of the need for compaction. This investigation aimed at exploring the potential use of the neurofuzzy (NF) approach to model the fresh and hardened properties of SCC containing pulverised fuel ash (PFA) as based on experimental data investigated in this paper. Twenty six mixes were made with water-to-binder ratio ranging from 0.38 to 0.72, cement content ranging from 183 to 317 kg/m3 , dosage of PFA ranging from 29 to 261 kg/m3 , and percentage of superplasticizer, by mass of powder, ranging from 0 to 1%. Nine properties of SCC mixes modeled by NF were the slump flow, JRing combined to the Orimet, JRing combined to cone, V-funnel, L-box blocking ratio, segregation ratio, and the compressive strength at 7, 28, and 90 days. These properties characterized the filling ability, the passing ability, the segregation resistance of fresh SCC, and the compressive strength. NF model is constructed by training and testing data using the experimental results obtained in this study. The results of NF models were compared with experimental results and were found to be quite accurate. The proposed NF models offers useful modeling approach of the fresh and hardened properties of SCC containing PFA.

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.

Transport Properties of Self-Consolidating Concrete

The permeability of concrete is influenced by the porosity and the interconnectivity of the pores in the cement paste and the microcracks in concrete, especially in the interface of paste-aggregate. The movements of gases, liquids, and ions through concrete is important because of their interactions with concrete constituents, including pore water, which can alter the integrity of concrete directly and indirectly, leading to the deterioration of structures. This study reports the findings from an investigation carried out to study the effect of the mixture variations on the durability of medium- and high-strength self-consolidating concrete (SCC). The mixture variations studied include the type of mineral admixtures, such as limestone powder (LSP) and pulverized fuel ash (PFA), and viscositymodifying admixtures (VMA) for both medium- and high-strength SCC. Air permeability, water permeability, capillary absorption, and chloride diffusivity were used to assess the durability of SCC mixtures in comparison with normal, vibrated concretes. The results showed that SCC mixtures, for medium- and high-strength grades using PFA followed by LSP, have lower permeability properties compared with normal concretes. SCC made with VMA had a higher sorptivity, air permeability, and water permeability compared with other SCC mixtures, which can be attributed to higher watercement ratio (w/c) and lack of pore filling effect. An in-place migration coefficient was obtained using the in-place ion migration test. This was used to compare the potential diffusivity of different concretes. The results indicated that SCC, for both grades of strength, made with PFA showed much lower diffusivity values in comparison with other mixtures, whereas the SCC mixtures with VMA showed higher diffusivity.

Durability of Self-Compacting Concrete

Because of the different mix design in comparison with traditional concrete and the absence of vibration, different durability characteristics might be expected for self-compacting concrete. The stateof- the-art report, prepared by RILEM Technical Committee TC 205-DSC focuses on the Durability of SCC, by first gathering the available information concerning pore structure, air-void system and transport mechanisms. The available durability results are studied and summarised keeping in mind the fundamental mechanisms and driving forces. All relevant durability issues are considered, like carbonation, chloride penetration, frost resistance, ASR, sulphate attack, thaumasite formation, fire resistance, etc... It is not the intention to give a review on these durability aspects for concrete in general. The aim however is to point at the specifics related to the use of SCC, e.g. due to the addition of a large amount of limestone filler, etc... This paper summarizes the main conclusions of the State-of-the-Art Report.

Casting of Self-Compacting Concrete

Increased productivity and improved working environment have had high priority in the development of concrete construction over the last decade. Development of a material not needing vibration for compaction—i.e. selfcompacting concrete (SCC)—has successfully met the challenge and is now increasingly being used in routine practice. The key to the improvement of fresh concrete performance has been nanoscale tailoring of molecules for surface active admixtures, as well as improved understanding of particle packing and of the role of mineral surfaces in cementitious matrixes. Fundamental studies of rheological behaviour of cementitious particle suspensions were soon expanded to extensive innovation programmes incorporating applied research, site experiments, instrumented full scale applications supporting technology, standards and guides, information efforts as well as training programmes. The major impact of the introduction of SCC is connected to the production process. The choice and handling of constituents are modified as well as mix design, batching, mixing and transporting. The productivity is drastically improved through elimination of vibration compaction and process reorganisation. The working environment is significantly enhanced through avoidance of vibration induced damages, reduced noise and improved safety. Additionally, the technology is improving performance in terms of hardened material properties like surface quality, strength and durability.

Bond and Interfacial Properties of Reinforcement in Self-Compacting Concrete

This paper reports a study carried out to assess the impact of the use of self-compacting concrete (SCC) on bond and interfacial properties around steel reinforcement in practical concrete element. The pull-out tests were carried out to determine bond strength between reinforcing steel bar and concrete, and the depth-sensing nano-indentation technique was used to evaluate the elastic modulus and micro-strength of the interracial transition zone (ITZ) around steel reinforcement. The bond and interracial properties around deformed steel bars in different SCC mixes with strength grades of 35 MPa and 60 MPa (C35, C60) were examined together with those in conventional vibrated reference concrete with the same strength grades. The results showed that the maximum bond strength decreased when the diameter of the steel bar increased from 12 to 20 mm. The normalised bond strengths of the SCC mixes were found to be about 10-40% higher than those of the reference mixes for both bar diameters (12 and 20 mm). The study of the interfacial properties revealed that the elastic modulus and the micro-strength of the ITZ were lower on the bottom side of a horizontal steel bar than on the top side, particularly for the vibrated reference concrete. The difference of ITZ properties between top and bottom side of the horizontal steel bar appeared to be less pronounced for the SCC mixes than for the corresponding reference mixes.

Assessment of Self-Compacting Concrete Immersed in Acidic Solutions

Ettringite and thaumasite can be found among the deterioration products of cementitious materials exposed to sulfate and hydrochloric attack. The results of a test program to investigate the acid resistance of self-compacting concrete (SCC) and conventional concrete (CC), immersed up to 18 weeks at 20°C in sulfuric and hydrochloric acid solutions, are described. The SCC was prepared with 47% carboniferous limestone powder, as a replacement for cement, and an ordinary portland cement. The CC was prepared with portland cement only. The water-binder ratios of the SCC and CC were 0.36 and 0.46, respectively. The parameter investigated was the time, in weeks, taken to cause 10% mass loss of fully immersed concrete specimens in a 1% solution of sulfuric acid and the same amount of loss in a 1% solution of hydrochloric acid. The investigation indicated that the SCC performed better than the CC in sulfuric solution but was slightly more vulnerable to hydrochloric acid attack compared to CC. The mode of attack between the two solutions was different.

Artificial Intelligence Model for Flowable Concrete Mixtures Used in Underwater Construction and Repair

This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.