This paper aims to employ combination of residual soil and Class F fly ash in developing a controlled low-strength material (CLSM), primarily used as backfilling material. In the mixture, surplus soil and concrete sand was blended well together with a given proportion of 6:4 by volume. Three levels of binder content (i.e. 80-, 100-and 130 kg/m3) and different percentages fly ash (i.e., 0%, 15%, 30%, and 45%) substituting to Portland cement were previously chosen for mix design. Several major engineering properties of the CLSM such as fresh density, flowability, setting time, water bleeding, unconfined compressive strength, and elastic modulus were investigated via a laboratory study. Testing results indicate that most of the proposed CLSM mixtures satisfy the requirements of excavatability as the 28-days compressive strength ranges from 0.3 to 1.4 MPa. In addition, increase in FA substituting to OPC resulted in workability improvement, setting time extension as well as compressive strength and elastic modulus reduction.

This paper presents two approaches, multiple linear regression (MLR) and artificial neural network (ANN), to develop predictive models for unconfined compressive strength of soil-based controlled low-strength material (CLSM). Our obtained laboratory data conducting on the soil-based CLSM were employed for analysis. Two strength prediction models were proposed: (1) strength is assumed to be a function of mix proportion and curing period; and (2) it is estimated from measured ultrasonic pulse velocity combined with effect of mixture parameters and curing ages. In each model, three predicted formulas were developed; one from MLR and two from ANN. It was showed that all the proposed equations have a well-predicted capacity.

Controlled low-strength materials (CLSMs) had been widely applied to excavation and backfill in civil engineering. However, the engineering properties of CLSM in these embankments vary dramatically due to different contents involved. This study is proposed to employ the ANSYS software and two different artificial neural networks (ANNs), that is, back-propagation artificial neural network (BPANN) and radial basis function neural network (RBFNN), to determine the engineering properties of CLSM by considering an inverse problem in which elastic modulus and the Poisson's ratio can be identified from inputting displacements and stress measurements. The PLANE42 element of ANSYS was first used to investigate a 2D problem of a retaining wall with embankment, with E = 0.02~3 GPa, ν= 0.1~0.4 to obtain totally 270 sampling data for two earth pressures and two top surface settlements of embankment. These data are randomly divided into training and testing set for ANNs. Practical cases of three kinds of backfilled materials, soil, and two kinds of CLSMs (CLSM-B80/30% and CLSM-B130/30%) will be used to check the validity of ANN prediction results. Results showed that maximal errors of CLSM elastic parameters identified by well-trained ANNs can be within 6%.