Stochastic Damage Constitutive Relationship of Steel-Reinforced Concrete Bond-Slip

The bond-slip damage of the interface between profile steel and concrete is the key point of steel-reinforced concrete structure. This paper is based on the statistical analysis of a large amount of experimental data and the distribution characteristics of bonding stress on the bonding surface of the profile steel and concrete, and the conversion rules between the three parts (chemical bonding force, frictional resistance, and mechanical interaction) of the bond force are obtained. According to the mutual conversion rules of the three parts of the bonding force on the steel-reinforced concrete bonding surface, a mesomechanical model based on the spring-friction block element is established. Taking into account the discreteness of concrete performance on the bonding surface and the randomness of defects, using the stochastic damage theory, a constitutive model of stochastic bonding damage on the steel-reinforced concrete bonding surface is established. The comparative analysis with the results of a large number of steel-reinforced concrete pull-out tests shows that the model can reasonably reflect the damage characteristics of the steel-reinforced concrete bonding surface.

A Novel Stochastic Approach for Static Damage Identification of Beam Structures Using Homotopy Analysis Algorithm

This paper proposes a new damage identification approach for beam structures with stochastic parameters based on uncertain static measurement data. This new approach considers not only the static measurement errors, but also the modelling error of the initial beam structures as random quantities, and can also address static damage identification problems with relatively large uncertainties. First, the stochastic damage identification equations with respect to the damage indexes were established. On this basis, a new homotopy analysis algorithm was used to solve the stochastic damage identification equations. During the process of solution, a static condensation technique and a L1 regularization method were employed to address the limited measurement data and ill-posed problems, respectively. Furthermore, the definition of damage probability index is presented to evaluate the possibility of existing damages. The results of two numerical examples show that the accuracy and efficiency of the proposed damage identification approach are good. In comparison to the first-order perturbation method, the proposed method can ensure better accuracy in damage identification with relatively large measurement errors and modelling error. Finally, according to the static tests of a simply supported concrete beam, the proposed method successfully identified the damage of the beam.

Endogenous stochastic optimisation for relief distribution assisted with unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have been increasingly viewed as useful tools to assist humanitarian response in recent years. While organisations already employ UAVs for damage assessment during relief delivery, there is a lack of research into formalising a problem that considers both aspects simultaneously. This paper presents a novel endogenous stochastic vehicle routing problem that coordinates UAV and relief vehicle deployments to minimise overall mission cost. The algorithm considers stochastic damage levels in a transport network, with UAVs surveying the network to determine the actual network damages. Ground vehicles are simultaneously routed based on the information gathered by the UAVs. A case study based on the Haiti road network is solved using a greedy solution approach and an adapted genetic algorithm. Both methods provide a significant improvement in vehicle travel time compared to a deterministic approach and a non-assisted relief delivery operation, demonstrating the benefits of UAV-assisted response.