Stability assessment of 3D reinforced soil structures under steady unsaturated infiltration

Internal stability assessment of geosynthetic-reinforced soil structures (GRSSs) has been commonly carried out assuming plane-strain conditions and dry backfills. However, failures of GRSSs usually show three-dimensional (3D) features and occur under unsaturated conditions. A procedure based on the kinematic limit-analysis method is proposed herein to assess 3D effects and the role of steady unsaturated infiltration on the required geosynthetic strength for GRSSs. A suction stress-based framework is used to describe the soil stress behavior under steady unsaturated infiltration. Based on the principle of energy-work balance, the required geosynthetic strength is determined. A comparison analysis with the prior research is conducted to verify the developed method. Two kinds of backfills, i.e., high-quality backfill and marginal backfill, are considered for comparison in this work. It is shown that accounting for 3D effects and the role of unsaturated infiltration considerably reduces the required geosynthetic strength. The 3D effects are primarily affected by the width-to-height ratio of GRSSs, and the contribution of unsaturated infiltration is mainly influenced by the soil type, flow rate, GRSS's height, and location of the water table.     

Anisotropic electrical and abrasion-sensing properties of cement-based composites containing aligned nickel powder

The electrical properties of aligned nickel powder-filled cement-based composites and the abrasion-sensing properties of these composites, as evaluated based on the change in their resistance at varying abrasion depths, were investigated in this paper. Micrograph characterization of nickel powder distribution indicates that the electrical conduction path preferentially forms along the direction of the nickel powder alignment, which leads to increasingly anisotropic electrical properties under greater magnetic field strengths. The level of anisotropy was also determined to be strongly dependent on the nickel powder content. The maximum anisotropic electrical properties were achieved at the percolation threshold content of the nickel powder, which is the critical point for the formation of an effective conductive network. Based on the cement-based composites anisotropic electrical properties and a sectionalized electrode design, the composites filled with aligned nickel powder demonstrated good abrasion-sensing properties, with humidity and temperature self-compensation abilities.     

Investigation on the full Mullins effect using time-dependent hyperelastic model with energy dissipation for rubber antivibration applications

Mechanics of Time-Dependent Materials - Despite being investigated for many years, the Mullins effect is considered a major obstacle in understanding the behavior of rubber, especially in...     

Cooperative differential evolution with fast variable interdependence learning and cross-cluster mutation

Cooperative optimization algorithms have been applied with success to solve many optimization problems. However, many of them often lose their effectiveness and advantages when solving large scale and complex problems, e.g., those with interacted variables. A key issue involved in cooperative optimization is the task of problem decomposition. In this paper, a fast search operator is proposed to capture the interdependencies among variables. Problem decomposition is performed based on the obtained interdependencies. Another key issue involved is the optimization of the subproblems. A cross-cluster mutation strategy is proposed to further enhance exploitation and exploration. More specifically, each operator is identified as exploitation-biased or exploration-biased. The population is divided into several clusters. For the individuals within each cluster, the exploitation-biased operators are applied. For the individuals among different clusters, the exploration-biased operators are applied. The proposed operators are incorporated into the original differential evolution algorithm. The experiments were carried out on CEC2008, CEC2010, and CEC2013 benchmarks. For comparison, six algorithms that yield top ranked results in CEC competition are selected. The comparison results demonstrated that the proposed algorithm is robust and comprehensive for large scale optimization problems.     

Optimizing horizontal alignment of roads in a specified corridor

Finding an optimal alignment connecting two end-points in a specified corridor is a complex problem that requires solving three interrelated sub-problems, namely the horizontal alignment, vertical alignment and earthwork optimization problems. In this research, we developed a novel bi-level optimization model combining those three problems. In the outer level of the model, we optimize the horizontal alignment and in the inner level of the model a vertical alignment optimization problem considering earthwork allocation is solved for a fixed horizontal alignment. Derivative-free optimization algorithms are used to solve the outer problem. The result of our model gives an optimal horizontal alignment in the form of a linear-circular curve and an optimal vertical alignment in the form of a quadratic spline. Our model is tested on real-life data. The numerical results show that our approach improves the road alignment designed by civil engineers by 27% on average, resulting in potentially millions of dollars of savings.     

Two pricing mechanisms for a service provider when customers’ delay costs are value-related

In this paper, we study two pricing mechanisms for a provider that serves delay-sensitive customers, one is the uniform pricing and the other is the priority auction. The expected delay cost of a customer is assumed to depend on his value for the service (i.e. the unit delay cost is a strictly increasing function of his value) and the expected sojourn time caused by the number of customers in the system (and his payment-based position in the queue if auction is adopted). Hence, each customer reacts to the service provider’s pricing mechanism by deciding whether or not to enter the service system and how much he pays. This resulting problem is a Stackelberg game. When auction is adopted, by using of adverse selection, we derive a feasible scheme in which customers with higher value would like to pay more. We further compare the performance of these two pricing mechanisms. Our numerical examples show that auction performs better not only in terms of revenue making but also in terms of social welfare improvement. Interestingly, auction can also render more customer surplus in most instances, which differs from the common techniques in revenue management field.     

Evaluation of pedestrian safety at intersections: A theoretical framework based on pedestrian-vehicle interaction patterns

Pedestrians are the most vulnerable road users, and pedestrian safety has become a major research focus in recent years. Regarding the quality and quantity issues with collision data, conflict analysis using surrogate safety measures has become a useful method to study pedestrian safety. However, given the inequality between pedestrians and vehicles in encounters and the multiple interactions between pedestrians and vehicles, it is insufficient to simply use the same indicator(s) or the same way to aggregate indicators for all conditions. In addition, behavioral factors cannot be neglected.To better use information extracted from trajectories for safety evaluation and pay more attention on effects of behavioral factors, this paper develops a more sophisticated framework for pedestrian conflict analysis that takes pedestrian-vehicle interactions into consideration. A concept of three interaction patterns has been proposed for the first time, namely ⿿hard interaction,⿿ ⿿no interaction,⿿ and ⿿soft-interaction.⿿ Interactions have been categorized under one of these patterns by analyzing profiles of speed and conflict indicators during the whole interactive processes. In this paper, a support vector machine (SVM) approach has been adopted to classify severity levels for a dataset including 1144 events extracted from three intersections in Shanghai, China, followed by an analysis of variable importance. The results revealed that different conflict indicators have different contributions to indicating the severity level under various interaction patterns. Therefore, it is recommended either to use specific conflict indicators or to use weighted indicator aggregation for each interaction pattern when evaluating pedestrian safety.The implementation has been carried out at the fourth crosswalk, and the results indicate that the proposed method can achieve a higher accuracy and better robustness than conventional methods. Furthermore, the method is helpful for better understanding underlying levels of safety from the behavioral perspective, which can also provide evidence for targeted traffic education on proper behaviors.     

A numerical database for ultrasonic defect characterisation using array data: Robustness and accuracy

Elastodynamic scattering matrices are known to contain geometrical information about a given scatterer, such as its size and shape. Here, the extent to which this scattered information can be retrieved using an ultrasonic array and used to characterise defects for Non-Destructive Evaluation is explored. Experimentally measured defect scattering matrices are compared to a database of possible scatterers and the nearest neighbour used to characterise the defect's geometry in terms of crack length and orientation. As an example, a database of scattering matrices for small (lengths 0.2–2.0 mm) cracks at a range of frequencies (2–20 MHz) is formed. The short range similarity (i.e. that between close neighbours) and the long range similarity (i.e. uniqueness) are used to understand the uncertainties inherent in this approach. In addition, the effect of spatially coherent noise, such as grain scattering in a polycrystalline metal, on the scattered information content is quantified. It is shown that as the noise level or frequency increases, so the information retrievable from a given crack is reduced, setting bounds on the accuracy of characterisation possible from a given ultrasonic dataset.     

Evolution of strength and failure of SCC during early hydration

The early strength evolution of self-consolidating concrete (SCC) is studied by a set of non-standard mechanical tests for compressive, tensile, shear and bending failure. The results are applicable in an industrial environment for process control, e.g. of slip casting with adaptive molds in robotic fabrication. A procedure for collapsing data to a master evolution curve is presented that allows to distinguish two regimes in the evolution. In the first, the material is capable of undergoing large localized plastic deformation, as expected from thixotropic yield stress fluids. This is followed by a transition to cohesive frictional material behavior dominated by crack growth. The typical differences in tensile and compressive strength of hardened concrete are observed to originate at the transition. Finally, the evolution of a limit surface in principal stress space is constructed and discussed.     

A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations

The duration of freeway traffic accidents duration is an important factor, which affects traffic congestion, environmental pollution, and secondary accidents. Among previous studies, the M5P algorithm has been shown to be an effective tool for predicting incident duration. M5P builds a tree-based model, like the traditional classification and regression tree (CART) method, but with multiple linear regression models as its leaves. The problem with M5P for accident duration prediction, however, is that whereas linear regression assumes that the conditional distribution of accident durations is normally distributed, the distribution for a “time-to-an-event” is almost certainly nonsymmetrical. A hazard-based duration model (HBDM) is a better choice for this kind of a “time-to-event” modeling scenario, and given this, HBDMs have been previously applied to analyze and predict traffic accidents duration. Previous research, however, has not yet applied HBDMs for accident duration prediction, in association with clustering or classification of the dataset to minimize data heterogeneity. The current paper proposes a novel approach for accident duration prediction, which improves on the original M5P tree algorithm through the construction of a M5P-HBDM model, in which the leaves of the M5P tree model are HBDMs instead of linear regression models. Such a model offers the advantage of minimizing data heterogeneity through dataset classification, and avoids the need for the incorrect assumption of normality for traffic accident durations. The proposed model was then tested on two freeway accident datasets. For each dataset, the first 500 records were used to train the following three models: (1) an M5P tree; (2) a HBDM; and (3) the proposed M5P-HBDM, and the remainder of data were used for testing. The results show that the proposed M5P-HBDM managed to identify more significant and meaningful variables than either M5P or HBDMs. Moreover, the M5P-HBDM had the lowest overall mean absolute percentage error (MAPE).