Investigation on the effect of cross beams in single span bridges under dynamic aspect by using finite element method

In the 1980s and 1990s, most bridges in Vietnam employed simple beam for short-span river crossings that did not need navigation and mainly used the reinforced concrete T-beams or pre-stressed concrete. While the T-frame structure that has hang single span is widely applied for rivers that require navigation...The single girder is a basic span made of pre-stressed reinforced concrete T or I cross section, with spacing ranging from 2.2m to 2.6m, and the absence of diaphragms was a common feature of bridges built during this time period. All horizontal crashes almost have happened on the spans that did not have diaphragms. As a result, the impact force is concentrated mostly on the lateral girders, leading to total damage. Thus, researches to evaluate the effect of diaphragms in the distribution of horizontal impact forces as well as minimizing the damage in the span structures are crucial. This study focuses on simulating and studying the influence of the number and position of the diaphragms in terms of stress, deformation and damage when a collision occurs in order to propose additional and repair solutions to enhance the horizontal resistance of span

Deep Neural Network Regression with Advanced Training Algorithms for Estimating the Compressive Strength of Manufactured-Sand Concrete

Manufactured sand has high potential for replacing natural sand and reducing the negative impact of the construction industry on the environment. This paper aims at developing a novel deep learning-based approach for estimating the compressive strength of manufactured-sand concrete. The deep neural networks are trained by  the advanced optimizers of Root Mean Squared Propagation, Adaptive Moment Estimation, and Adaptive Moment Estimation with Nesterov momentum (Nadam). In addition, the activation functions of logistic sigmoid, hyperbolic tangent sigmoid, and rectified linear unit activation are employed. A dataset including 132 samples has been used to train and verify the deep neural networks. Stone powder content, sand ratio, quantity of cement, quantity of water, quantity of coarse aggregate, quantity of water-reducer, quantity of manufactured sand, concrete slump, unit weight of concrete, and curing age are  utilized as predictor variables. Based on experiments, the Nadam-optimized model used with the sigmoid activation function has achieved the most desired performance with root mean square error (RMSE) = 1.95, mean absolute percentage error (MAPE) = 3.04%, and coefficient of determination (R2) = 0.97. Thus, this neural computing model is recommended for practical purposes because it can help to mitigate the time and cost dedicated to laboratory work

Leadership Flaws and Organizational Stages

The paper builds on academic work as well as keynote statements about leadership by prolific figures in research and academics, while examining common leadership flaws and exploring ways to negate them. It describes three stages of a Values Journey, namely pre-orderly, orderly and post-orderly, and within those differentiates six steps, representing followers’ values, each having a typical leadership approach. A summary of academic literature surrounding common leadership flaws and organizational pathologies and an overview of a model depicting followers’ coping mechanisms are provided in support of the paper’s main arguments. Popular leadership principles are examined and related to five basic pillars of intent, developed by the authors, to support effective leadership

Groutability prediction of microfine cement based soil improvement using evolutionary LS-SVM inference model

Permeation grouting is a widely used technique for soil improvement in construction engineering. Thus, predicting the results of the grouting activity is a particularly interesting topic that has drawn the attention of researchers both from the academic field and industry. Recent literature has indicated that artificial intelligence (AI) approaches for groutability prediction are capable of delivering better performance than traditional formula-based ones. In this study, a novel AI method, evolutionary Least Squares Support Vector Machine Inference Model for groutability prediction (ELSIM-GP), is proposed to forecast the result of grouting activity that utilizes microfine cement grout. In the model, Least Squares Support Vector Machine (LS-SVM) is a supervised machine learning technique that is employed to learn the decision boundary for classifying high dimensional data. Differential Evolution (DE) is integrated into ELSIM-GP for automatically optimizing its tuning parameters. 240 historical cases of grouting process for sandy silt soil have been collected to train, validate, and test the inference model. Experimental results demonstrated that ELSIM-GP can overcome other benchmark approaches in terms of forecasting accuracy. Therefore, the proposed approach is a promising alternative for predicting groutability

Interval estimation of construction cost at completion using least squares support vector machine

Completing a project within the planned budget is the bottom-line of construction companies. To achieve this goal, periodic cost estimation is vitally important not only in the planning phase, but also in the execution phase. Due to high uncertainty in operational environment, point estimation of project cost is oftentimes not sufficient to assist the decision-making process. This study utilizes Least Squares Support Vector Machine (LS-SVM), machine learning based interval estimation (MLIE), and Differential Evolution (DE) to establish a novel model for predicting construction project cost. LS-SVM is a supervised learning technique used for regression analysis. MLIE is employed for inference of prediction intervals. Moreover, our model deploys DE in the cross validation process to search for the optimal values of tuning parameters. The newly developed model, named as EAC-LSPIM, yields results consisting of a point estimate coupled with lower and upper prediction limits, at a certain level of confidence, to accentuate uncertainty. Simulation and performance comparison demonstrate that the new model is capable of delivering accurate and reliable forecasting results

Computer Vision-Based Recognition of Pavement Crack Patterns Using Light Gradient Boosting Machine, Deep Neural Network, and Convolutional Neural Network

The performance and serviceability of asphalt pavements have a direct influence on people's daily lives. Timely detection of pavement cracks is crucial in the task of periodic pavement survey. This paper proposes and verifies a novel computer vision-based method for recognizing pavement crack patterns. Image processing techniques, including Gaussian steerable filters, projection integrals, and image texture analyses, are employed to characterize the surface condition of asphalt pavement roads. Light Gradient Boosting Machine, Deep Neural Network, and Convolutional Neural Network are employed to recognize various patterns including longitudinal, transverse, diagonal, minor fatigue, and severe fatigue cracks. A dataset, including 12,000 samples, has been collected to construct and verify the computer vision-based approaches. Based on experiments, it can be found that all three machine learning models are capable of delivering good categorization results with an accuracy rate > 0.93 and Cohen's Kappa coefficient > 0.76. Notably, the Light Gradient Boosting Machine has achieved the most desired performance with an accuracy rate > 0.96 and Cohen's Kappa coefficient > 0.88

Fuzzy clustering chaotic-based differential evolution for resource leveling in construction projects

 Project scheduling is an important part of project planning in many management companies. Resource lev­eling problem describes the process of reducing the fluctuations in resource usage over the project duration. The goal of resource leveling is to minimize the incremental demands that cause fluctuations of resources, and thus avoid unde­sirable cyclic hiring and firing during project execution. In this research, a novel optimization model, named as Fuzzy Clustering Chaotic-based Differential Evolution for solving Resource leveling (FCDE-RL), is introduced. Fuzzy Cluster­ing Chaotic-based Differential Evolution (FCDE) is developed by integrating original Differential Evolution with fuzzy c-means clustering and chaotic techniques to tackle complex optimization problems. Chaotic was exploited to prevent the optimization algorithm from premature convergence. Meanwhile, fuzzy c-means clustering acts as several multi-par­ent crossover operators to utilize the information of the population efficiently to enhance the convergence. Experimental results revealed that the new optimization model is a promising alternative to assist project managers in dealing with construction project resource leveling. First published online: 13 Jul 201

A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence

This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction (FLSVR TSP ). The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR's tuning parameters, the FLSVR TSP incorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that the FLSVR TSP has achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction

A Multi-Hazard Safety Evaluation Framework for a Submerged Bridge using Machine Learning Model

This study proposes a submerged bridge safety evaluation process against seismic and flood hazards. Due to the uncertainties in the scours, seismic hazard, and structural performance for a given seismic excitation are inevitable, reliability analysis is adopted. A machine-learning based scour risk curve, which is established by the multivariate adaptive regression splines (MARS) incorporated with firefly algorithm (FA), is built to reflect the flood hazard. The seismic hazard is measured using a code-based probabilistic seismic hazard curve. A series of nonlinear time-history analyses are performed to determine the structural performance under different peak-ground-acceleration values. Displacement ductility is used to measure the bridge performance under attacks of both hazards. The influence of the immersed water depth on a bridges performance is investigated. A case study, in which the nonlinear behaviors in concrete (including core and cover areas), steel bar and soil are included in a bridge model, is conducted to illustrate the proposed methodology and the structural performances with added mass are investigated to show the submerged water effect. According to the results obtained, highly variability of seismic performances is observed and it is important to include the immersed water depth to capture the seismic capacity of a bridge if the submerged bridge depth is great