An empirical, stress‐history‐based model for the small strain (or elastic) shear modulus $G^e$ of cohesionless soils has been developed. Various sands were tested in an 18 cm diameter oedometer equipped with lateral‐stress measuring capabilities and bender elements for propagating and recording elastic waves through the specimens. While previous efforts have found $G^e$ scaling with approximately the 1/2 power of confining stress $(n=0.5)$, the present work shows $n$ on virgin loading $n_1$ ranging from 0.39 to 0.72. Furthermore, $n_1$ was shown to be related to the maximum dimensionless stiffness coefficient for 1‐D strain $\left(S_{1\mathrm{Dmax}}\right)$. The values of $n$ on unloading and reloading $n_u$ displayed a much narrower range than $n_l$. The average value of $n_u$ was 0.38, a value much closer to the theoretical $n=1/3$ for perfect spheres. This is attributed to the smaller changes in number and quality of intergranular contacts during unloading and reloading than during virgin compression. The developed model for $G^e$ includes overconsolidation ratio (OCR) terms for the horizontal and vertical directions. However, to implement it, an estimate for the OCR term can be made based solely on the in situ $K_0$.

The spectral analysis of surface waves (SASW) method is a nondestructive in situ seismic testing method for determining shear wave velocity profiles of soil sites and stiffness profiles of pavement systems. The key steps involved are construction of an experimental dispersion curve from data collected in situ, and inversion of the dispersion curve to determine the stiffness profile. In this paper, an automated method for the construction of dispersion curves is discussed. Weighted least‐squares best‐fit solution is used to estimate the phase at each frequency with coherence as weighing function. By knowing the distance between the receivers and the phase at each frequency, phase velocity and wavelength associated with that frequency are calculated. These raw data are then combined using the least‐absolute‐valuebest‐fit criterion to construct a dispersion curve. The inversion process for determination of stiffness profile from the dispersion curve is described in a companion paper.

The spectral‐analysis‐of‐surface‐waves (SASW) method is a nondestructive testing method based upon generation and detection of elastic stress waves. The key steps involved are construction of an experimental dispersion curve from data collected in situ, and inversion of the dispersion curve to determine the stiffness profile. An automated algorithm for determining the representative dispersion curve from field data has been presented in a companion paper. In most applications, the determination of the layer moduli are accomplished by using a manual trial and error process (forward modeling). Unfortunately, this process is rather time‐consuming and requires engineering judgement. To improve this aspect of SASW testing, an automated technique based upon the general inverse theory is described here. The technique provides a fast procedure for simultaneously determining shear wave velocity and thickness of different layers. In addition, some description of uncertainty in the determined results is provided.

The results of a theoretical, experimental, and numerical investigation into the volume‐change behavior of an unsaturated clay are described. The theoretical approach adopted is presented and its implementation detailed. The work is shown to be analogous to Terzaghi's classical theory of one‐dimensional consolidation for saturated soils. An experimental procedure is proposed for the determination of appropriate one‐dimensional soil properties. The procedure is used to determine the variation of volumetric strain and volumetric moisture content with respect to capillary potential for the Kimmeridge clay under consideration. The experimental results achieved are used to obtain deformation moduli that are applied in a simulation of seasonal ground movement. In particular, ground movements corresponding to previously determined moisture‐content variations over the 1983 period are predicted. The results of the numerical simulation are assessed qualitatively and quantitatively by comparison with field‐measured data. The approach adopted is concluded to be capable of producing realistic predictions of seasonal ground movement.

In this paper, a procedure for calculating the size distribution of rock fragments using video images is described. The procedure utilizes a high‐resolution video camera for image capturing in the field and a set of computer algorithms for processing the video images. The computer program first delineates the individual rock fragments in the images. This is followed by statistical procedures that take into account fragment overlap and the two‐dimensional nature of the images. The computer algorithms can process many images to produce a single size distribution curve, and takes into account sample variability as well as combining images taken at different scales. All the procedures described have been implemented into a single computer program. By comparing the computer procedures with laboratory experiments, the accuracy of the method is demonstrated.