construction sector of Pakistan has rapidly developed with a higher growth rate of 9.05 percent during the period 2016-2017. However, a poor record of completing the projects in allocated time, cost, and with desired ...
详细信息
This article reviews the state of the research on a novel and cost-effective strengthening technique for substandard reinforced concrete (RC) structures that uses Post-Tensioned Metal Straps (PTMS). The technique appl...
详细信息
Compared with traditional financing mode of construction, public-private-partnership (PPP) mode has the great opportunity that private enterprises develop rapidly and solved the shortcomings that the amount of infrast...
详细信息
Passive (battery-free) wireless patch antenna sensors have been developed in recent years for strain sensing, to provide convenient and low-cost instrumentation. Despite past efforts, current analytical and experiment...
详细信息
Vacuum assisted Resin Transfer Molding (VaRTM) as a composite fabricating technique can be used to apply Carbon Fiber (CF) sheets on cracked steel structures. This paper deals with the fatigue durability of typical we...
详细信息
Due to the application of reinforced anchor technology in soil reinforcement, the anchor after long-term use will have serious corrosion, which greatly affects the safety of the engineering structure, leading to engin...
Due to the application of reinforced anchor technology in soil reinforcement, the anchor after long-term use will have serious corrosion, which greatly affects the safety of the engineering structure, leading to engineering accidents. In this paper, GFRP (Glass Fiber Reinforced Polymer) anchor is used instead of reinforced bolt for the pull-out experiment. In order to investigate the deformation of the soil around the GFRP anchor solid in the process of drawing, this paper improves the traditional indoor model pull-out experiment of GFRP anchor and introduces PIV (Particle Image Velocimetry) technology and its application to the analysis of soil deformation in the process of drawing.
The construction industry has been consistently performing poorly in safety resulting in substantial human and economic losses globally. The hazard related to heavy machinery operations can lead to injury or fatality ...
The construction industry has been consistently performing poorly in safety resulting in substantial human and economic losses globally. The hazard related to heavy machinery operations can lead to injury or fatality of operators, workers, or visitors. Thus, safety training of heavy machinery operators (HMOs) is crucial to perform their tasks safely on construction projects. HMOs face various risks due to complex work environments at a job site such as the presence of other workers, material flow, equipment motion, and temporary structures that limit the space for heavy machinery. This research identifies the causes of accidents and competencies required to eliminate these accidents associated with heavy machinery operators. To achieve that, interview data with fifteen construction project managers are analyzed. This study's findings suggest that the causes of heavy machinery accidents include insufficient maintenance, negligence of operators, inadequate training, human factor, and site condition. Conversely, the competencies to mitigate heavy machinery accidents are knowledge of safety incentives and penalties from safety training, the ability to conduct safety briefing, inspect heavy machinery and site conditions, and communication skills. Industry practitioners and researchers can use these findings to enhance construction productivity by creating a safe working environment at construction sites.
Modern construction material research is picking impetus in the recent two decades; a greater number of admixtures and combinations were tried by bountiful researchers across the globe. In this work an attempt is made...
详细信息
ISBN:
(纸本)9781538694336
Modern construction material research is picking impetus in the recent two decades; a greater number of admixtures and combinations were tried by bountiful researchers across the globe. In this work an attempt is made to obtain the strength characteristics by using Soft computing techniques in the marble and quarry dust impregnated concrete. Strength characteristics of concrete is studied with reference to the addition of the above-mentioned admixtures and the results were given as input parameters. 28 days compressive strength of concrete with varying marble and quarry dust content is utilized as input data for the neural network and a model is created which is used to predict the strength. To prepare the ANN model the results are taken and the values obtained are mean square propagation and the testing, training, validation and for overall propagation the values are 0.99793, 0.99577, 0.9927 and 0.99073 and the best validation performance is 0.023295 at epoch 7 respectively for MD and for QD the values are 0.9974, 0.94374, 0.94445 and 0.947 and the best validation performance is 0.035578 at epoch 4 respectively. It is found that neural network can be utilized effectively to predict the strength characteristics of concrete.
It has become a mainstream to use physical models to quantify expected energy savings from alternative retrofit methods and technologies. However, they are not suitable for predicting energy use of buildings when deta...
It has become a mainstream to use physical models to quantify expected energy savings from alternative retrofit methods and technologies. However, they are not suitable for predicting energy use of buildings when detailed and specified input parameters are unavailable. The overall purpose of the research is to support the stakeholders in taking decisions on refurbishments options when not all of physical information is available, in order to achieve the Swedish Energy Agency's measurements of near-zero energy buildings. The research will transfer big data from Swedish Energy Performance Certificates for building retrofitting. A Support Vector Machines and Fuzzy C-means clustering (SVM-FCM) integrated machine learning algorithm is used directly to extract the case-specific knowledge from EPC big data regarding building characteristics and energy saving of retrofit measures. It enables to prioritize retrofit measures and compute their expected energy savings for buildings. This proposed data driven method is an attempt of taking advantage of big data for practical building retrofit selection.
Strengthening and retrofitting of existing reinforced concrete (RC) elements have been gaining interest in recent decades. Among the strengthening solutions available, fiber reinforced composites present certain advan...
详细信息
暂无评论