Liquid crystalline polymers and elastomers

The results of experiments and theory correlate increasingly well in the investigation of liquid crystalline polymers. Current work focuses on rheology and the behavior of blends of thermotropic LC polymers, due to reemerging economic interest in these materials. Research on liquid crystalline elastomers concentrates on potential applications such as artificial muscles and lasing.     

Delamination detection in CFRP laminates with embedded small-diameter fiber Bragg grating sensors

Newly developed small-diameter fiber Bragg grating (FBG) sensors were applied for the detection of the delamination in carbon fiber reinforced plastic (CFRP) cross-ply laminates. Since the diameter of the FBG sensor was very small, the sensor was easily embedded into the 0° ply along the reinforcing carbon fibers. The reflection spectra from the FBG sensor were measured at various delamination lengths through four-point bending test. The form of the spectrum changed sensitively, as the delamination length increased. Moreover, the spectrum was calculated theoretically for confirmation of the measured spectrum. The calculated result reproduced the change in the measured spectrum very well. Then, we proposed the intensity ratio in the spectrum as an effective indicator for the prediction of the delamination length. This indicator has a potential to be applied to other laminate configurations.     

A web-based screening model for climate risk to water supply systems in the northeastern United States

The aim of this study is to describe the development and application of a web-based decision support tool (ViRTUE) for performing climate risk evaluations of water supply systems. The tool is designed for small-scale water utilities in the northeastern United States that may lack the resources for detailed climate change risk investigations. Development of this tool demonstrates a relatively new approach to web application development using the Shiny framework for the R programming language to create an interactive environment for stakeholders and water managers to explore climate vulnerabilities. Using a decision-scaling framework, the tool allows the user to perform a climate stress test to evaluate the performance and vulnerability to water supply shortfalls of local reservoir systems over a wide range of potential climate change scenarios using a generic systems model. Probabilities of future climate conditions derived from climate projections then help inform utility operators of impending risk.     

Age-dependent size effect and fracture characteristics of ultra-high performance concrete

This paper presents an investigation of the age-dependent size effect and fracture characteristics of ultra-high performance concrete (UHPC). The study is based on a unique set of experimental data connecting aging tests for two curing protocols of one size and size effect tests of one age. Both aging and size effect studies are performed on notched three-point bending tests. Experimental data are augmented by state-of-the-art simulations employing a recently developed discrete early-age computational framework. The framework is constructed by coupling a hygro-thermo-chemical (HTC) model and the Lattice Discrete Particle Model (LDPM) through a set of aging functions. The HTC component allows taking into account variable curing conditions and predicts the maturity of concrete. The mechanical component, LDPM, simulates the failure behavior of concrete at the length scale of major heterogeneities. After careful calibration and validation, the mesoscale HTC-LDPM model is uniquely posed to perform predictive simulations. The ultimate flexural strengths from experiments and simulations are analyzed by the cohesive size effect curves (CSEC) method, and the classical size effect law (SEL). The fracture energies obtained by LDPM, CSEC, SEL, and cohesive crack analyses are compared, and an aging formulation for fracture properties is proposed. Based on experiments, simulations, and size-effect analyses, the age-dependence of size effect and the robustness of analytical-size effect methods are evaluated.     

Structural performance of ballastless track slabs reinforced with BFRP and SFCB

Because of the inductive impedance caused by steel meshes in traditional reinforced ballastless track slabs, the electrical properties, primarily the rail resistance and inductance, of jointless track circuits are affected by electromagnetic induction between the slabs and the electric current in the rail. This problem results in poor transmission performance throughout the track circuit. Insulating sleeves or cards between the steel meshes have been used to improve the insulation capability of steel meshes in slabs; however, they reduce the bonding performance between the steel bars and concrete. Because of the good insulation properties of fiber-reinforced polymer composite bars (FRPs) and steel-fiber reinforced polymer composite bars (SFCBs), these composite materials have shown potential to overcome this insulation problem. However, the structural performance of the ballastless track slabs reinforced by basalt fiber reinforced polymer composite bars (BFRPs) and SFCBs, which play a key role in the structure and transportation safety, needs to be investigated. In this paper, six ballastless track slabs reinforced with BFRPs, SFCBs, and steel bars were constructed and tested. The following results were obtained. (1) Shear failures were observed for all slabs, both the BFRP and SFCB slabs meet the load level requirements, and SFCBs reinforcements have higher strength utilization compared with BFRPs reinforcements. (2) The bond-quality of SFCBs and BFRPs reinforcements proved slightly poorer than that of the steel bars. Because of the good corrosion resistance of the FRP, the maximum crack width limits can be slightly larger than that of the RC slabs. (3) Bischoff’s equation was initially used to calculate the deflection of partially prestressed concrete slabs under service loads. The results demonstrated a good agreement between the theoretical and experimental analysis. (4) Considering the tensile stiffness, the modified ACI equation was used to calculate the slabs’ crack width and the theoretical and experimental results showed a good agreement.     

Pre-strain effect of on fracture performance of high-strength steel welds

Fracture toughness of pre-strain effect was determined as a function of the temperature in structural steels of the 600 to 780 MPa class. Cyclic loading during earthquakes produces pre-strain in the component, which is enhanced at the region of strain concentration. During the Kobe Great Earthquake in 1995 in Japan, 10 to 15 % pre-strain was recorded at the beam-to-column connection. The relationship between critical CTOD and CGHAZ length was sampled by fatigue pre-crack for pre-strained HAZ, which is a significant decrease compared to that of the base metal. Furthermore, the effect of pre-strain is discussed in terms of the CTOD and Charpy impact energy of the local brittle zone.     

Evaluating performance of daylight-linked building controls during preliminary design

Thanks to the spread of new light sources and of smart dynamic control systems, automation sector has begun to play a fundamental role in lighting design. In this regard, daylight-linked control systems (DLCSs) represent a particularly interesting research field, since they offer great opportunities both in obtaining energy savings and in improving visual comfort conditions. However, their use is not so spread, because of the difficulties in predicting their functioning during the design process and in evaluating their effective energetic and economic advantages: available technical solutions are so many that design choices can be very hard for specialists. To overcome these obstacles, a precise assessment methodology is needed. Given these premises, the goal of the paper is to show the effectiveness of new performance parameters (Daylight Integration Adequacy, Percentage Intrinsic Light Excess, Percentage Light Waste and Percentage Light Deficit) in order to evaluate DLCs performance and to underline which factors mostly affect their functioning.     

A novel approach for sand liquefaction prediction via local mean-based pseudo nearest neighbor algorithm and its engineering application

The prediction method plays crucial roles in accurate prediction of sand liquefaction. Recently, machine learning has been widely used for prediction of sand liquefaction, and the Local Mean-based Pseudo Nearest Neighbor (LMPNN) algorithm, one of machine learning techniques, showed good performance in pattern recognition. In this study, we propose a sand liquefaction prediction model based on the LMPNN algorithm, which is the first work of applying the LMPNN algorithm to sand liquefaction prediction. Then, our proposed prediction model is used for evaluation of site liquefaction grade in Tongzhou District of China. And the comparison between our proposed prediction model with the liquefaction evaluation method in the Chinese code is made, which will provide an important approach to predicting the sand liquefaction grades for the major construction project sites. Extensive experiments on grade prediction demonstrate that the effectiveness of our proposed prediction model based on the LMPNN algorithm. In addition, shaking table test of an engineering site model is conducted for evaluating whether this engineering site model is liquefaction and non-liquefaction or not. And the experiment result of the shaking table test is the same as that of our proposed prediction model based on LMPNN algorithm, which further demonstrates the effectiveness of our proposed prediction model. Consequently, our proposed prediction model is proved to have a good prospect of engineering application in the liquefaction prediction.     

Mapping the barriers of AI implementations in the public distribution system: The Indian experience

With increasing digitization, private organizations have started adopting modern technology to improve the effectiveness and transparency of their system. In countries like India, where most public services are under government control, technology adoption is nascent due to various obstacles. The study considered artificial intelligence (AI) the most popular technology and identified 18 critical adoption barriers in India's public distribution systems (PDS). The study is further extended to find the contextual relationship among barriers using interpretative structural modeling (ISM) and prioritize them using the analytical network process (ANP) method. The study identifies lack of trust in technology, lack of AI literacy, and political issues as significant barriers to AI adoption in PDS. The hybrid methodology used in this study proposed five different strategies for effective and smooth implementation of AI in PDS, which would help the policymaker plan the same.     

Do innovation in environmental-related technologies cyclically and asymmetrically affect environmental sustainability in BRICS nations?

This study contributes to the current research by identifying the cyclical and asymmetrical impact of innovation in environmental-related technologies on carbon dioxide emissions in BRICS economies, as well as control variables such as fossil fuels, export taxes, household consumption expenditures, and exports, for the period 1990(Q1)-2016 (Q4). Following are some of the key findings. First, the data analysis indicated a significant long-term positive link between negative shocks to innovation in environmental-related technologies and carbon dioxide emissions during the economic downturn. Second, positive shocks to innovation in environmental-related technologies minimize carbon dioxide emissions during the economic upturn. Third, the relationship between innovation shocks in environmental-related technologies and carbon dioxide emissions was counter-cyclical during business cycles. Fourth, impact of positive shocks to innovation in environmental-related technologies on carbon dioxide emissions was more than the impact of negative shocks to innovation in environmental-related technologies on carbon dioxide emissions. Fifth, increasing the export tax serve as an effective measure to mitigate carbon dioxide emissions. Sixth, an increase in exports, use of fossil fuels, and household consumption expenditures contributed to the carbon dioxide emissions. Based on estimated results, the paper suggests policy implications for policymakers to reduce carbon dioxide emissions.