In order to investigate the influence of basalt fibers (BFs) on the mechanical performance of recycled aggregate concrete (RAC), some groups of RAC specimens were first tested involving different types of fibers such as carbon fibers, steel fibers, polypropylene fibers and hybrid fibers. The main four indices for the investigation consisted of cube compressive strengths, axial compressive strengths, splitting tensile strengths and Young’s modulus. The effects of fiber volume fractions on the RAC slumps were also discussed. Meanwhile, the mechanical properties and failure modes of the BF-reinforced RAC were compared with those of other fiber-reinforced RAC and common concrete (CC). Subsequently the optimal volume fractions of BFs were explored for different mechanical properties within the volume fraction range of 0–0.2%. The back propagation neural networks were further applied to predict and validate the optimal BF fractions. Lastly, the general strength formulas, as well as the elastic modulus formula, for BF-reinforced RAC were deducted based on the specimen test results. It is found that the addition of fibers may improve the failure modes of RAC and different fibers present positive or negative effects on the mechanical properties. The optimal volume fractions of BF with respect to the four mechanical indices are 0.1%, 0.15%, 0.1% and 0.2% respectively. The proposed strength and elastic modulus formulas of BF-reinforced RAC provide satisfactory predictions with the test results and thus can be used as a reference in practice.

Truss structures have been widely adopted for civil structures such as long-span buildings and bridges. An actual truss system is usually statically indeterminate having numerous members and high redundancy. It is practically difficult to evaluate the truss safety through traditional reliability-based approaches in view of complex failure modes and uncertainties. Moreover, monitoring data are generally insufficient in reality due to limited sensors under cost consideration. Therefore, a nested discrete Bayesian network has been developed for safety evaluation of truss structures. A concept of member risk coefficient is first proposed based on the mechanical relationship between load effects and member resistance. According to the coefficients of all members, member risk sequences are found as the basis for establishing the topology of a member-level Bayesian network. Each network node represents a truss member and a nodal variable having three states: elasticity, plasticity, and failure. Two relevant member nodes are connected by a directed edge whose causality strength is expressed by a conditional probability table. Meanwhile, a system-level network topology is established to reflect the effects of member states on the truss system. The system is assigned with a node having two states: safety and failure. The directed edge of each member node directly points to the system node. Then, the two networks are combined to form a nested network topology. By this means, direct topology learning is avoided in order to find rational and concise topologies satisfying the mechanical characteristics of civil structures. After that, the conditional probability tables for the nested network are obtained through parameter learning on complete numerical observation data. The data acquirement procedure takes into account uncertainties by defining the randomness of cross-sectional areas and external loads. With the conditional probability tables, the nested network is ready for use. When new evidence from limited monitored members is input into the nested network, the state probabilities of the other members, as well as the system, are simultaneously updated using exact inference algorithms. The inference ability using insufficient information well accords with the demand of engineering practice. Finally, the proposed method has been successfully verified against both numerical and experimental truss structures. It was found that the network estimations could be further confirmed with more evidence.