The broadband bonus: Estimating broadband Internet's economic value

Machine learning for structural engineering: A state-of-the-art review

Fixed and mobile broadband substitution in Sweden

Deep learning-based axial capacity prediction for cold-formed steel channel sections using Deep Belief Network

Concrete filled steel tubes (CFSTs) show advantageous applications in the field of construction, especially for a high axial load capacity. The challenge in using such structure lies in the selection of many parameters constituting CFST, which necessitates defining complex relationships between the components and the corresponding properties. The axial capacity (Pu) of CFST is among the most important mechanical properties. In this study, the possibility of using a feedforward neural network (FNN) to predict Pu was investigated. Furthermore, an evolutionary optimization algorithm, namely invasive weed optimization (IWO), was used for tuning and optimizing the FNN weights and biases to construct a hybrid FNN–IWO model and improve its prediction performance. The results showed that the FNN–IWO algorithm is an excellent predictor of Pu, with a value of R2 of up to 0.979. The advantage of FNN–IWO was also pointed out with the gains in accuracy of 47.9%, 49.2%, and 6.5% for root mean square error (RMSE), mean absolute error (MAE), and R2, respectively, compared with simulation using the single FNN. Finally, the performance in predicting the Pu in the function of structural parameters such as depth/width ratio, thickness of steel tube, yield stress of steel, concrete compressive strength, and slenderness ratio was investigated and discussed.

https://www.mdpi.com/1996-1944/13/5/1205/pdf

Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression.

Cellular beams are an attractive option for the steel construction industry due to their versatility in terms of strength, size, and weight. Further benefits are the integration of services thereby reducing ceiling-to-floor depth (thus, building’s height), which has a great economic impact. Moreover, the complex localized and global failures characterizing those members have led several scientists to focus their research on the development of more efficient design guidelines. This paper aims to propose an artificial neural network (ANN)-based formula to precisely compute the critical elastic buckling load of simply supported cellular beams under uniformly distributed vertical loads. The 3645-point dataset used in ANN design was obtained from an extensive parametric finite element analysis performed in ABAQUS. The independent variables adopted as ANN inputs are the following: beam’s length, opening diameter, web-post width, cross-section height, web thickness, flange width, flange thickness, and the distance between the last opening edge and the end support. The proposed model shows a strong potential as an effective design tool. The maximum and average relative errors among the 3645 data points were found to be 3.7% and 0.4%, respectively, whereas the average computing time per data point is smaller than a millisecond for any current personal computer.

Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams

Cellular beams are an attractive option for the steel construction industry due to their versatility in terms of strength, size, and weight. Further benefits are the integration of services thereby reducing ceiling-to-floor depth (thus, building’s height), which has a great economic impact. Moreover, the complex localised and global failures characterizing those members have led several scientists to focus their research on the development of more efficient design guidelines. This paper aims to propose an artificial neural network (ANN)-based formula to estimate the critical elastic buckling load of simply supported cellular beams under uniformly distributed vertical loads. The 3645-point dataset used in ANN design was obtained from an extensive parametric finite element analysis performed in ABAQUS. The independent variables adopted as ANN inputs are the following: beam’s length, opening diameter, web-post width, cross-section height, web thickness, flange width, flange thickness, and the distance between the last opening edge and the end support. The proposed model shows a strong potential as an effective design tool. The maximum and average relative errors among the 3645 data points were found to be 3.7% and 0.4%, respectively, whereas the average computing time per data point is smaller than a millisecond for any current personal computer.

/pdf/neural-network-based-formula-for-the-buckling-load-1dlb3l3wil.pdf

In Portugal, the telecommunications incumbent offers broadband access to the Internet, both through digital subscriber line and cable modem In this article, we estimate the impact on broadband access to the Internet of the structural separation of these two businesses We use a panel of consumer level data and a discrete choice model to estimate the price elasticities of demand and the marginal costs of broadband access to the Internet Based on these estimates, we simulate the effect on prices and social welfare of the structural separation Our results indicate that the structural separation would lead to substantial price reductions For broadband clients, on average, each household would save 3:37 euros per month, or 14% of the current price levels Overall, on average, each household would save 2:73 euros per month, or 14% of the current price levels We test the robustness of our results in terms of: (i) the estimates of the demand elasticities, (ii) the strategic behavior of the firms, and (iii) the market share estimates There is no evidence of collusion

/pdf/impact-on-broadband-access-to-the-internet-of-the-dual-4iiu42n8wh.pdf

Impact on Broadband Access to the Internet of the Dual Ownership of Telephone and Cable Networks

Topology Optimisation is a broad concept deemed to encapsulate different processes for computationally determining structural materials optimal layouts. Among such techniques, Discrete Optimisation has a consistent record in Civil and Structural Engineering. In contrast, the Optimisation of Continua recently emerged as a critical asset for fostering the employment of Additive Manufacturing, as one can observe in several other industrial fields. With the purpose of filling the need for a systematic review both on the Topology Optimisation recent applications in structural steel design and on its emerging advances that can be brought from other industrial fields, this article critically analyses scientific publications from the year 2015 to 2020. Over six hundred documents, including Research, Review and Conference articles, added to Research Projects and Patents, attained from different sources were found significant after eligibility verifications and therefore, herein depicted. The discussion focused on Topology Optimisation recent approaches, methods, and fields of application and deepened the analysis of structural steel design and design for Additive Manufacturing. Significant findings can be found in summarising the state-of-the-art in profuse tables, identifying the recent developments and research trends, as well as discussing the path for disseminating Topology Optimisation in steel construction.

Topology Optimisation in Structural Steel Design for Additive Manufacturing

Several suppliers of oil and gas (O & G) equipment and services have reported the necessity of making frequent resources planning adjustments due to the variability of demand, which originates in unbalanced production levels. The occurrence of these specific problems for the suppliers and operators is often related to the bullwhip effect. For studying such a problem, a research proposal is herein presented. Studying the bullwhip effect in the O & G industry requires collecting data from different levels of the supply chain, namely: services, upstream and midstream suppliers, and downstream clients. The first phase of the proposed research consists of gathering the available production and financial data. A second phase will be the statistical treatment of the data in order to evaluate the importance of the bullwhip effect in the oil and gas industry. The third phase of the program involves applying artificial neural networks (ANN) to forecast the demand. At this stage, ANN based on different training methods will be used. Further on, the attained mathematical model will be used to simulate the effects of demand fluctuations and assess the bullwhip effect in an oil and gas supply chain.

https://www.mdpi.com/2504-4990/1/3/57/pdf

Tiago Ribeiro

Papers

Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams

Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams

Impact on Broadband Access to the Internet of the Dual Ownership of Telephone and Cable Networks

Topology Optimisation in Structural Steel Design for Additive Manufacturing

Using Machine Learning for Enhancing the Understanding of Bullwhip Effect in the Oil and Gas Industry