This paper focuses on the flexural behavior of RC beams externally strengthened with Carbon Fiber Reinforced Polymers (CFRP) fabric. A non-linear finite element (FE) analysis strategy is proposed to support the beam flexural behavior experimental analysis. A development system (QUEBRA2D/FEMOOP programs) has been used to accomplish the numerical simulation. Appropriate constitutive models for concrete, rebars, CFRP and bond-slip interfaces have been implemented and adjusted to represent the composite system behavior. Interface and truss finite elements have been implemented (discrete and embedded approaches) for the numerical representation of rebars, interfaces and composites.

Regression analysis (RA) can establish an explicit formula to predict the strength of High-Performance Concrete (HPC); however, the accuracy of the formula is poor. Back-Propagation Networks (BPNs) can establish a highly accurate model to predict the strength of HPC, but cannot generate an explicit formula. Genetic Operation Trees (GOTs) can establish an explicit formula to predict the strength of HPC that achieves a level of accuracy in between the two aforementioned approaches. Although GOT can produce an explicit formula but the formula is often too complicated so that unable to explain the substantial meaning of the formula. This study developed a Backward Pruning Technique (BPT) to simplify the complexity of GOT formula by replacing each variable of the tip node of operation tree with the median of the variable in the training dataset belonging to the node, and then pruning the node with the most accurate test dataset. Such pruning reduces formula complexity while maintaining the accuracy. 404 experimental datasets were used to compare accuracy and complexity of three model building techniques, RA, BPN and GOT. Results show that the pruned GOT can generate simple and accurate formula for predicting the strength of HPC.

Dredged silt from reservoirs in southern Taiwan was sintered to make lightweight aggregates (LWA), which were then used to produce lightweight aggregate concrete (LWAC).This study aimed to assess the compressive strength and homogeneity of LWAC using ultrasonic-echo sensing. Concrete specimens were prepared using aggregates of four different particle density, namely 800, 1100, 1300 and 2650 kg/$m^3$. The LWAC specimens were cylindrical and a square wall with core specimens drilled. Besides compressive strength test, ultrasonic-echo sensing was employed to examine the ultrasonic pulse velocity and homogeneity of the wall specimens and to explore the relationship between compressive strength and ultrasonic pulse velocity. Results show that LWA, due to its lower relative density, causes bloating, thus resulting in uneven distribution of aggregates and poor homogeneity. LWAC mixtures using LWA of particle density 1300 kg/$m^3$ show the most even distribution of aggregates and hence best homogeneity as well as highest compressive strength of 63.5 MPa. In addition, measurements obtained using ultrasonic-echo sensing and traditional ultrasonic method show little difference, supporting that ultrasonic-echo sensing can indeed perform non-destructive, fast and accurate assessment of LWAC homogeneity.

Due to the reduction of bond strength resulting from the high corrosion level of reinforcing bars, influence of this reduction on flexural capacity of reinforced concrete (RC) beam should be considered. An extreme case is considered, where bond strength is complete lost and/or the tensile steel are exposed due to heavy corrosion over a fraction of the beam length. A compatibility condition of deformations of the RC beam with partially unbonded length is proposed. Flexural capacity of this kind of RC beam is predicted by combining the proposed compatibility condition of deformations with equilibrium condition of forces. Comparison between the model's predictions with the experimental results published in the literature shows the practicability of the proposed model. Finally, influence of some parameters on the flexural capacity of RC beam with partially unbonded length is discussed. It is concluded that the flexural capacity of the beam may not be influenced by the completely loss of bond of the whole beam span as long as the tensile steel can yield; whether or not the reduction of the flexural capacity of the beam resulting from the loss of bond over certain length may occur depends on the detailed parameters of the given beam.

This study aims to develop a cost-based high-performance concrete (HPC) mix optimization system based on an integrated approach using artificial neural networks (ANNs) and genetic algorithms (GA). ANNs are used to predict the three main properties of HPC, namely workability, strength and durability, which are used to evaluate fitness and constraint violations in the GA process. Multilayer back-propagation neural networks are trained using the results obtained from experiments and previous research. The correlation between concrete components and its properties is established. GA is employed to arrive at an optimal mix proportion of HPC by minimizing its total cost. A system prototype, called High Performance Concrete Mix-Design System using Genetic Algorithm and Neural Networks (HPCGANN), was developed in MATLAB. The architecture of the proposed system consists of three main parts: 1) User interface; 2) ANNs prediction models software; and 3) GA engine software. The validation of the proposed system is carried out by comparing the results obtained from the system with the trial batches. The results indicate that the proposed system can be used to enable the design of HPC mix which corresponds to its required performance. Furthermore, the proposed system takes into account the influence of the fluctuating unit price of materials in order to achieve the lowest cost of concrete, which cannot be easily obtained by traditional methods or trial-and-error techniques.