An experimental study on the shear strength development of cement‐sand‐gravel (CSG) material was carried out using triaxial compression tests. The effects of the cementing agent content, aggregate content, and gradation on the shear strength of CSG material were analyzed. The shear strength remarkably increased with increasing cementing agent content and aggregate content for a given confining pressure. The increase in shear strength with increasing cementing agent content far exceeded that with increasing aggregate content. However, the stress‐strain curves and shear strength changed only slightly when the aggregate gradation for CSG material was adjusted. Based on the test data, a strength criterion for CSG material is proposed as a function of the cementing agent content, aggregate content, and shear strength of the aggregate gradation.

Modifying the admixture of alkali-activated cementitious materials using components such as fly ash and fine sand may reduce CO₂ emissions and conserve natural resources and energy. This study adopted strength testing, scanning electron microscopy, and mercury intrusion porosimetry to investigate the influence of different admixtures on the compressive strength and flexural strength of alkali slag cementing materials and the microstructure characteristics of hardened slurry under the action of load. The flexural strength of alkali slag cement slurry and mortar was reduced by replacing slag powder with fly ash. Content of fine sand less than 20% had little effect on the strength of alkali slag cement mortar; however, when the content of fine sand exceeded 30%, the strength decreased significantly. The hydration degree at 3 d was large, and the density of slurry increased with the extension of age. Increased fly ash or fine sand content decreased the density of the slurry, and increased fly ash resulted in a large number of unhydrated fly ash particles in the cementitious materials. Addition of fine sand resulted in a large number of microcracks in the slurry, which gradually decreased with the extension of hydration age.

Knowing the deformation properties of cemented sand and gravel (CSG) material can help construct reasonable constitutive models for the material, which can be used to simulate the structural performance of various practical projects including CSG dams. In this study, to investigate the effect of cement content on the deformation properties of CSG material, we employ triaxial compressive tests for cement contents of 20, 40, 60, 80, and 100 kg/m³ with a confining pressure range of 0.3–1.2 MPa, and theoretically analyze the results by the regression analysis prediction method. Here, we show that both cement content and confining pressure influence the deformation properties of CSG material: for an increase in cement content, the failure strain decreases and brittleness of CSG material increases; the initial modulus of the CSG material increased exponentially with increasing cement content or confining pressure; the peak volumetric strain and its corresponding axial strain increase linearly with increasing confining pressures, which decrease with increasing cement content; the initial tangent volumetric ratio can also be determined by the peak volumetric strain and its corresponding axial strain.

In this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O–D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O–D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O–D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O–D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen–Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O–D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O–D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and “true” O–D demands is relatively small, and the O–D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O–D demands with fine accuracy.