Boundary conditions of precast concrete frames play a crucial role in their progressive collapse resistance under middle column removal scenarios. Interior and exterior frames behave differently as a result of different horizontal restraints on side columns. This paper presents an experimental study on four precast concrete frames with either 90° bend or lap-splice of the bottom reinforcement in the beam-column joint. The resistance of interior and exterior frames were determined quantitatively. Reinforcement detailing in the beam-column joint dominated structural resistance and deformation capacity of interior frames. Side columns of interior frames remained intact under compressive arch action (CAA) and catenary action, in spite of significant horizontal deflections and severe cracking because of initial horizontal compression at the CAA stage and subsequent tension at the catenary action stage. However, one exterior frame exhibited flexural failure of the side column when subject to catenary action, evidenced by concrete crushing in the compression zone. In side beam-column joints, diagonal shear cracks were observed at the CAA stage, and shear links were mobilized to sustain the shear force. Finally, relevant conclusions on the progressive collapse design of precast concrete frames are drawn from the experimental findings.

This paper describes an experimental investigation on the resistance and failure mode of four precast concrete frames. Each frame comprised a middle beam-column joint, a double-span beam, and two side columns. Two parameters, namely, reinforcement detailing in the beam-column joint and dimensions of the side columns, were investigated in the experimental program. Frame specimens were loaded on the middle joint by a vertical servohydraulic actuator, and corresponding horizontal reaction forces were recorded through load cells connected to the side columns. Experimental results and observations indicate that precast concrete frames were capable of developing compressive arch action (CAA) at the initial loading stage. However, frames with smaller side columns exhibited shear failure in the side joint. This hindered the development of catenary action in the bridging beam. By increasing the dimensions of the side columns, significant catenary action developed in the frames, and eventually flexural fail...

Concrete mix design is one of the most critical issues in concrete technology. This process aims to create a concrete mix which helps deliver concrete with desired features and quality. Contemporary requirements for concrete concern not only its structural properties, but also increasingly its production process and environmental friendliness, forcing concrete producers to use both chemically and technologically complex concrete mixtures. The concrete mix design methods currently used in engineering practice are joint analytical and laboratory procedures derived from the Three Equation Method and do not perform well enough for the needs of modern concrete technology. This often causes difficulties in predicting the final properties of the designed mix and leads to precautionary oversizing of concrete properties for fear of not providing the required parameters. A new approach that would make it possible to predict the newly designed concrete mix properties is highly desirable. The answer to this challenge can be methods based on machine learning, which have been intensively developed in recent years, especially in predicting concrete compressive strength. Machine learning-based methods have been more or less successful in predicting concrete compressive strength, but they do not reflect well the variability that characterises the currently used concrete mixes. A new adaptive solution that allows estimating concrete compressive strength on the basis of the concrete mix main ingredient composition by including two observations for a given batch of concrete is proposed herein. In presented study, a machine learning model was built with a deep neural network architecture, trained on an extensive database of concrete recipes, and translated into a mathematical formula. Testing on four concrete mix recipes was performed, which were calculated according to contemporary design methods (Bolomey and Fuller method), and a comparative analysis was conducted. It was found out that the new algorithm performs significantly better than that without adaptive features trained on the same dataset. The presented algorithm can be used as a concrete strength checking tool for the concrete mix design process.