The American Society for Testing and Materials (ASTM) is an independent organization devoted to the development of standards.

American Society for Testing and Materials

https://eprints.lancs.ac.uk/70554/1/boyaci_zografos_geroliminis_postprint.pdf

An optimization framework for the development of efficient one-way car-sharing systems

Short-term traffic parameter forecasting is critical to modern urban traffic management and control systems. Predictive accuracy in data-driven traffic models is reduced when exposed to non-recurring or non-routine traffic events, such as accidents, road closures, and extreme weather conditions. The analytical mining of data from social networks – specifically twitter – can improve urban traffic parameter prediction by complementing traffic data with data representing events capable of disrupting regular traffic patterns reported in social media posts. This paper proposes a deep learning urban traffic prediction model that combines information extracted from tweet messages with traffic and weather information. The predictive model adopts a deep Bi-directional Long Short-Term Memory (LSTM) stacked autoencoder (SAE) architecture for multi-step traffic flow prediction trained using tweets, traffic and weather datasets. The model is evaluated on an urban road network in Greater Manchester, United Kingdom. The findings from extensive empirical analysis using real-world data demonstrate the effectiveness of the approach in improving prediction accuracy when compared to other classical/statistical and machine learning (ML) state-of-the-art models. The improvement in predictive accuracy can lead to reduced frustration for road users, cost savings for businesses, and less harm to the environment.

/pdf/a-deep-learning-model-for-urban-traffic-flow-prediction-with-2hgicbbh53.pdf

A Deep-Learning Model for Urban Traffic Flow Prediction with Traffic Events Mined from Twitter

https://www.zenodo.org/record/1445781/files/Nardi_et_al_2018.pdf

Quantification of heat energy losses through the building envelope: A state-of-the-art analysis with critical and comprehensive review on infrared thermography

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2018WR023332

A Review of Radar-Rain Gauge Data Merging Methods and Their Potential for Urban Hydrological Applications

Due to a growing concern with the assessment of the thermal performance of building envelopes, in particular during energy audits and energy certification processes, it has become increasingly important to use inspection techniques to determine the thermal transmittance values (U-value) of building elements. One of the techniques that may be explored to evaluate the thermal behaviour of the envelope is Infrared thermography (IRT). As the accuracy of the thermal transmittance estimation is dependent on the state of the heat transfer, having steady-state or near steady-state conditions is crucial to get reliable measurements and a stable temperature difference between inside and outside is mandatory. With this in mind the authors intend to evaluate the application of IRT to U-value determination studies. For this reason, a set of laboratory measurements were performed. A steady temperature difference was imposed in 4 different homogeneous materials used to build a box that is heated using a thermal resistance. IRT temperatures were measured on the outside surfaces of the box, and are then used to estimate the U-value. Those results are compared with the U-value calculated using the thermal conductivity obtained by means of the guarded hot plate method.

/pdf/laboratory-assessment-of-thermal-transmittance-of-1hnc3fnfad.pdf

Laboratory assessment of thermal transmittance of homogeneous building elements using infrared thermography

The computational efficiency of the FEA is strongly dependent on the algorithmic and numerical efficiency of the FE solver. This is particularly important in case of implicit FE codes, such as DD3IMP, the in-house static implicit FE solver under analysis in this work. This study describes the procedure adopted to identify the main computational bottlenecks of the FE solver in order to introduce the OpenMP directives and, consequently, to achieve a major speedup of the whole algorithm. The different parallelized branches of the code are tested using the well-known square cup deep drawing example, considering different FE discretizations. The analysis of the preliminary results, concerning the CPU wall time, allows to demonstrate that the adoption of HPC techniques, such as the abovementioned OpenMP directives, enables to: (i) achieve a speedup factor close to the number of cores (in a single computer); (ii) solve a problem in a shorter time; (iii) solve a bigger problem in the same amount of time and, thus, (iv) achieve a better solution in a given amount of time.

Improving Computational Performance through HPC Techniques: case study using DD3IMP in-house code

The mechanical behaviour of non-chiral and chiral single-walled carbon nanotubes under tensile and bending loading conditions is investigated. For this purpose, three-dimensional finite element modelling is used in order to evaluate the tensile and bending rigidities and, subsequently, the Young's moduli. It is shown that the evolution of rigidity, tensile and bending, as a function of diameter can be described by a unique function for non-chiral and chiral single-walled nanotubes, i.e. regardless of the index or angles of chirality. A comprehensive study of the influence of the nanotube wall thickness and diameter on the Young's modulus values is also carried out. It is established that the evolution of the Young's modulus as a function of the inverse of the wall thickness follows a quasi-linear trend for nanotubes with diameters larger than 1.085 nm. The current numerical simulation results are compared with data reported in the literature. This work provides a benchmark in relation to ascertaining the mechanical properties of chiral and non-chiral single-walled carbon nanotubes by nanoscale continuum models.

Mechanical characterization of single-walled carbon nanotubes: Numerical simulation study

Short-term surface flood modelling requires reliable estimation of the distribution of floods over urban catchments with sufficient lead time in order to provide timely warnings. In this paper new improvements to the traditional Support Vector Machine (SVM) prediction technique for rainfall prediction are presented. The results obtained using the new improvements, such as enhancement of SVM prediction using Singular Spectrum Analysis (SSA) for pre-processing the data and combined SSA and SVM with a statistical analysis that give stochastic results to AI-based prediction, are compared with the results obtained using the SVM technique only. When applying the SVM technique to the rainfall data used in this study, the results showed an underestimation of the rainfall peaks. When using SSA for preprocessing the rainfall data the results are significantly better. The new stochastic approach proved to be useful for estimating the level of confidence of the forecast.

https://spiral.imperial.ac.uk:8443/bitstream/10044/1/14699/2/2011_Simoes%20et%20al_SSA%2bSVM_ICUD.pdf

A coupled SSA-SVM technique for stochastic short-term rainfall forecasting

A more reliable flood forecasting could benefit from higher-resolution rainfall forecasts as inputs. However, the prediction lead time of the operational rainfall forecasting models will substantially diminish while sub-hourly (e.g., 5-min) rainfall forecasting is required. A method that integrates the SVM (Support Vector Machine) and Cascade-based downscaling techniques is therefore developed in this work to carry out high-resolution (5-min) precipitation forecasting with longer lead time (45-60 minutes). The 5-min raingauge observations from Coimbra (Portugal) are employed to assess the proposed methodology. A comparison with the conventional SVM is also conducted to study the possible benefit of using the proposed methodology to carry out short-term rainfall forecasting.

/pdf/an-enhanced-blend-of-svm-and-cascade-methods-for-short-term-2432l54i0p.pdf

An enhanced blend of SVM and Cascade methods for short-term rainfall forecasting

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