CDM Smith is an engineering and construction company which provides solutions in water, energy, transportation, and facilities projects for government and private clients. The employee owned company is currently ranked 22nd on Engineering News-Record's 2015 Top 500 Design Firms list and 13th on their 2015 Top 200 Environmental Firms list.
One of the key challenges in shallow geothermal technology is improving the thermal performance of ground heat exchangers (GHEs) while reducing operational costs. Existing enhancement techniques are typically applied uniformly along the GHE depth, despite field data showing non-uniform thermal performance. This paper presents a spatiotemporal characterization of a single u-pipe GHE, consolidating theoretical framework, laboratory characterization, and numerical modeling to identify where and when coupled heat and moisture transfer becomes significant within the GHE geometry. The coupled model is validated against a laboratory column test under ambient, and non-insulated boundary conditions and applied to characterize the three-dimensional axial and radial extent of soil drying and wetting around a GHE operating in heat rejection mode. Results reveal an asymmetric temperature and moisture distribution, with greater desaturation near the inlet leg, concentrated in the upper 40–50% of the GHE depth and extending 1.21 m - 1.52 m (4 - 5 ft) radially from the pipe. This desaturation is self-limiting, stabilizing within one to two months of operation, and corresponds to an approximately fivefold increase in matric suction near the pipe, from approximately 348 kPa (7268.13 psf) to approximately 1851 kPa (38,658.94 psf) over four months. The associated reduction in thermal conductivity and heat capacity substantially alters the heat exchange capacity of the surrounding soil. These findings establish the spatiotemporal basis for future design and operational strategies, such as targeted use of high-conductivity materials near the GHE inlet and periodic flow reversal, to improve the cost-effectiveness of shallow geothermal systems.
The compression index (Cc) is a key parameter for predicting consolidation settlement in fine-grained soils. In geotechnical practice, empirical correlations based on simple index properties are widely used as a fast and economical alternative to laboratory oedometer tests. However, most existing formulations were developed under site-specific conditions, raising concerns about their reliability when applied beyond their original scope. In this study, the predictive performance of 20 widely used empirical correlations for Cc estimation is evaluated using a comprehensive global database comprising 1008 soil samples compiled from multiple countries and geological settings worldwide. The dataset spans wide ranges of liquid limit (17.1–199.0%), plasticity index (2.0–82.0%), initial void ratio (0.279–7.114), natural water content (8– 244.1%), and Cc (0.013–2.2). Correlations are assessed for the complete dataset and for different compressibility ranges using multiple performance metrics, with Theil’s inequality coefficient adopted as the main ranking criterion. Results show that most traditional correlations exhibit large prediction errors and high dispersion when applied globally, particularly for low-compressibility soils. A simple correlation recently proposed by the authors, based solely on natural water content, demonstrates competitive performance, negligible bias, and improved robustness, offering a practical alternative for preliminary Cc estimation in data-scarce conditions.
The United States is allotting USD 15 billion for lead service line replacement in proportion to documented lead, the largest of a growing class of environmental-indicator transfers. Reconstructing the rule exactly, we show it prices looking below zero. Resolving an unknown pipe lowers a state's expected allotment in three of the eight jurisdictions we can measure above the statutory floor, and raises it in none. Below it the allotment cannot move. Systems that file nothing are scored lead-free. We prove no fixed formula on self-reported inventories can pay for need without punishing its measurement, derive the unique additive repair – an audit-anchored, strategy-proof estimator – and calibrate it on the program's data. Discovery is repriced from a median loss of USD 48.63 per line per year to zero. Allocation swings fall from 16.5
This study proposes advanced stacking ensemble machine learning approaches to predict soil Liquidity Index (LI) and Undrained Shear Strength (Su). Over 1,550 LI and 950 Su measurements from global research were employed. Individual regressors—Multilayer Perceptron (MLP), Random Forest (RF), and AdaBoost—were evaluated alongside ensemble strategies, including Simple Averaging, Weighted Averaging, and stacking using a Support Vector Regression (SVR) meta-learner. Hyperparameter tuning was performed using both Bayesian Optimization (BO) and Particle Swarm Optimization (PSO). AdaBoost achieved the best results for LI prediction, while RF yielded the highest accuracy for Su. Weighted Averaging ensemble methods produced outstanding predictive performances, achieving R2 values of 0.9913 (BO) and 0.9961 (PSO) for Su, and 0.9624 (BO) and 0.9338 (PSO) for LI. BO proved superior for LI models, while PSO excelled for Su models. The results highlight the robust potential of ensemble modeling and tailored optimization in the field of geotechnical engineering.
