Behavior of RC beam-column joint is very complex as the composite material behaves differently in elastic and inelastic range. The approaches generally used for predicting joint shear strength are either based on theoretical, strut-and-tie or empirical methods. These approaches are incapable of predicting the accurate response of the joint for entire range of loading. In the present study a new generalized RC beam-column joint shear strength model based on hybrid approach i.e. combined strut-and-tie and empirical approach has been proposed. The contribution of governing parameters affecting the joint shear strength under compression has been derived from compressive strut approach whereas; the governing parameters active under tension has been extracted from empirical approach. The proposed model is applicable for various conditions such as, joints reinforced either with or without shear reinforcement, joints with wide beam or wide column, joints with transverse beams and slab, joints reinforced with X-bars, different anchorage of beam bar, and column subjected to various axial loading conditions. The joint shear strength prediction of the proposed model has been compared with 435 experimental results and with eleven popular models from literature. In comparison to other eleven models the prediction of the proposed model is found closest to the experimental results. Moreover, from statistical analysis of the results, the proposed model has the least coefficient of variation. The proposed model is simple in application and can be effectively used by designers.

Structural health monitoring is important for the safety of lives and asset management. In this study, numerical models were developed for the piezoresistive behavior of smart concrete based on finite element (FE) method. Finite element models were calibrated with experimental data collected from compression test. The compression test was performed on smart concrete cube specimens with 75 mm dimensions. Smart concrete was made of cement CEM II 42.5 R, silica fume, fine and coarse crushed limestone aggregates, brass fibers and plasticizer. During the compression test, electrical resistance change and compressive strain measurements were conducted simultaneously. Smart concrete had a strong linear relationship between strain and electrical resistance change due to its piezoresistive function. The piezoresistivity of the smart concrete was modeled by FE method. Twenty-noded solid brick elements were used to model the smart concrete specimens in the finite element platform of Ansys. The numerical results were determined for strain induced resistivity change. The electrical resistivity of simulated smart concrete decreased with applied strain, as found in experimental investigation. The numerical findings are in good agreement with the experimental results.

In the present study, effect of size and placement of cubic specimens on compressive strength of self-compacting lightweight concrete (SCLC) were considered. To do so, 81 specimens of different sizes (50 mm, 75 mm, 100 mm, and 150 mm) were prepared by using three different mixes of SCLC. Results of the cured specimens were then used in regression analyses to find predictive equations with regard to both the placement direction and the size. Test results showed that the strength ratio in cases in which the direction of loading and placement were parallel, were higher than those specimens, whose configurations were normal between loading and placement. In addition, strength ratios in SCLC mixes were slightly higher than those are for self-compacting normal weight concrete. In order to analyze the effect of size on compressive strength the conventional size effect law as well as the modified size effect law (MSEL) were used. Besides, the convergence criterion of nonlinear regression process of size effect study has been discussed. Analyses of the results showed that the unconstraint nonlinear regression in size effect study of SCLC mixes could lead to erroneous results.

The alarming rate of depletion of natural stone based coarse aggregates is a cause of great concern. The coarse aggregates occupy nearly 60-70% by volume of concrete being produced. Research efforts are on to look for alternatives to stone based coarse aggregates from sustainability point of view. Response surface methodology (RSM) is adopted to study and address the effect of ferrochrome slag (FCS) replacement to coarse aggregate replacement in the ordinary Portland cement (OPC) based concretes. RSM involves three different factors (ground granulated blast furnace slag (GGBS) as binder, flyash (FA) as binder, and FCS as coarse aggregate), with three different levels (GGBS (0, 15, and 30%), FA (0, 15, and 30%) and FCS (0, 50, and 100%)). Experiments were carried out to measure the responses like, workability, density, and compressive strength of FCS based concretes. In order to optimize FCS replacement in the OPC based concretes, three different traditional optimization techniques were used (grey relational analysis (GRA), technique for order of preference by similarity (TOPSIS), and desirability function approach (DFA)). Traditional optimization techniques were accompanied with principal component analysis (PCA) to calculate the weightage of responses measured to arrive at the final ranking of replacement levels of GGBS, FA, and FCS in OPC based concretes. Hybrid combination of PCA-TOPSIS technique is found to be significant when compared to other techniques used. 30% GGBS and 50% FCS replacement in OPC based concrete was arrived at, to be optimal.

As rice husk ash (RHA) is not produced in controlled manufacturing process like cement, its properties vary significantly even within the same lot. In fact, properties of Rice Husk Ash Based Concrete (RHABC) are largely dictated by uncertainty leading to huge deviations from their expected values. This paper proposes a Robust Cost Optimization (RCO) procedure for RHABC, which minimizes such unwanted deviation due to uncertainty and provides guarantee of achieving desired strength and workability with least possible cost. The RCO simultaneously minimizes cost of RHABC production and its deviation considering feasibility of attaining desired strength and workability in presence of uncertainty. RHA related properties have been modeled as uncertain-but-bounded type as associated probability density function is not available. Metamodeling technique is adopted in this work for generating explicit expressions of constraint functions required for formulation of RCO. In doing so, the Moving Least Squares Method is explored in place of conventional Least Square Method (LSM) to ensure accuracy of the RCO. The efficiency by the proposed MLSM based RCO is validated by experimental studies. The error by the LSM and accuracy by the MLSM predictions are clearly envisaged from the test results. The experimental results show good agreement with the proposed MLSM based RCO predicted mix properties. The present RCO procedure yields RHABC mixes which is almost insensitive to uncertainty (i.e., robust solution) with nominal deviation from experimental mean values. At the same time, desired reliability of satisfying the constraints is achieved with marginal increment in cost.

Cracks are an important distress of concrete bridges, and may reduce the life and safety of bridges. However, the traditional manual crack detection means highly depend on the experience of inspectors. Furthermore, it is time-consuming, expensive, and often unsafe when inaccessible position of bridge is to be assessed, such as viaduct pier. To solve this question, the real-time automatic crack detecting system with unmanned aerial vehicle (UAV) become a choice. This paper designs a new automatic detection system based on real-time comprehensive image processing for bridge crack. It has small size, light weight, low power consumption and can be carried on a small UAV for real-time data acquisition and processing. The real-time comprehensive image processing algorithm used in this detection system combines the advantage of connected domain area, shape extremum, morphology and support vector data description (SVDD). The performance and validity of the proposed algorithm and system are verified. Compared with other detection method, the proposed system can effectively detect cracks with high detection accuracy and high speed. The designed system in this paper is suitable for practical engineering applications.

Apart from strength properties, durability, toughness and workability are also important criteria in defining the performance of a concrete structure. Hence "High Performance Concrete (HPC)" is introduced. It is different from high strength concrete and can have various applications. In this paper, the properties (Mechanical and Durability) of High Performance Concrete blended with bagasse ash at 5%, 10%, 15% and 20% are studied. However, it is difficult to analyze the performance based on different properties obtained from different experiments. Hence it is necessary to combine all the criteria/properties into a single value to obtain a result by a technique called Analytical Hierarchy Process (AHP).It is an effective tool for dealing with complex decision making, and may aid the decision maker to set priorities and make the best decision. In addition, the AHP incorporates a useful technique for checking the consistency of the decision maker?s evaluations, thus reducing the bias in the decision making process.