Intercomparison of Data Transformation Methods for the Assessment of Extreme Rainfall
Testing and Evaluation of RoadBounce - Mobile Phone App based Technology for Road Roughness Measurement
Mechanical Properties of Engineered Cementitious Composite using Polyvinyl Alcohol Fibers
A Study on the Lane Validating the Distribution Factor on Urban and Rural Highways
Experimental Investigations on Bond Performance of GFRP Bars
Estimating the Soil Moisture Index using Normalized Difference Vegetation Index (NDVI) And Land Surface Temperature (LST) for Bidar and Kalaburagi District, Karnataka
Roughness Evaluation of Flexible Pavements Using Merlin and Total Station Equipment
Site Suitability Analysis for Solid Waste Dumping in Ranchi City, Jharkhand Using Remote Sensing and GIS Techniques
Unsaturated Seepage Modeling of Lined Canal Using SEEP/W
Strengthening and Rehabilitation of RC Beams with Openings Using CFRP
A Seasonal Autoregressive Model Of Vancouver Bicycle Traffic Using Weather Variables
Prediction of Compressive Strength of Concrete by Data-Driven Models
Predicting the 28 Days Compressive Strength of Concrete Using Artificial Neural Network
Measuring Compressive Strength of Puzzolan Concrete by Ultrasonic Pulse Velocity Method
Design and Analysis of Roller Compacted Concrete Pavements for Low Volume Roads in India
In this study, maintenance solutions for 19 airport pavements in New Mexico are derived based on Pavement Condition Index (PCI) and nonlinear deterioration rate. In a Pavement Management System (PMS), PCI indicates the functional condition of the pavement. In this study, a specific maintenance treatment is applied when the PCI value of a pavement section reaches a minimum defined value or cutoff value. Using system dynamics modeling, modules to quantify the benefit and Life Cycle Cost (LCC) were developed and utilized to determine the relative benefit and life cycle treatment cost of a maintenance solution or treatment. This study indicates that airports with higher initial PCI have lower functional benefit and lower LCC for maintenance solutions of different PCI improvement or PCI rises. Benefit and cost are determined using two different system dynamic modules developed in Powersim and then benefit and cost are compared using developed design charts. Benefit cost ratio (BCR) design charts are capable of showing the BCR for airport pavements having initial PCI 30 to 80, cutoff-PCI 10 to 80 and rise 10 to 40. For PCI rise 30 and 40, initial PCI 60, 70 and 80 have shown almost the same BCR for a different cutoff-PCI.
Roller Compacted Concrete (RCC) is an innovative pavement material for the construction of low volume rural roads. RCC can easily overcome the problems usually observed in the construction of flexible bituminous pavements. RCC is the commercial name used for concrete placed with conventional hot mix bituminous paving equipment compacted with vibratory rollers.RCC pavements are highly rigid and hence eliminates the high deformation problems such as rutting and corrugations generally encountered in flexible pavements. For rural development in India, connectivity of rural roads is an important aspect; but many rural roads such as ODR (Other District Roads) and VR (Village Roads) are of poor quality, potholed, and unable to withstand the loads of heavy farm equipment. Two construction techniques are available i.e., rigid and flexible. Of these, selection of type of construction depends on the sub-grade soil types, rainfall, traffic pattern and availability of construction materials. In the present paper, the design and analysis of RCC Pavements have been considered in place of conventional Cement Concrete Pavements and Bituminous pavements. The flexural strengths of Roller compacted concrete of 4.5MPa, 5.0MPa and 5.5MPa are considered for design and analysis. Design curves for low volume roads are presented. Proposed RCC pavement is suitable for sub-grade having low modulus of reaction.
The aim of this study is prediction of 28-day compressive strength of concrete by data-driven models. Hence, by considering concrete constituents as input variables, two data-driven models namely Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are constructed for the purpose of predicting the 28-days compressive strength of different concrete mix designs. Comparing the two models illustrates that MLR model is not a suitable model for predicting the compressive strength; however, ANN can be used to efficiently predict the compressive strength of concrete.
A study is carried out to determine the crop water requirement of some selected crops for the command area in Kunigal taluk. These crops include rice, pulses, groundnut, sugarcane and millet (ragi). Crop water requirement for each of the crops is determined by using 30-year climatic data in CROPWAT. Reference crop evapotranspiration (ETo) is determined using the FAO Penman Monteith method. For all the crops considered, three decades: decades I, II, and III and seven crop growth stages: nursery, nursery / land preparation, land preparation, initial stage, development stage, mid-season and late season stage are considered. The study shows that for the area under study, reference evapotranspiration (ETo) varied from 3.11 to 5.29 mm/day. Crop evapotranspiration (ETc) and crop water requirement for sugarcane varied from 1.61mm/day to 3.46mm/day and 0.0 mm/dec to 51.9 mm/dec, for ragi (millet) from 1.44 mm/day to 3.46 mm/day and 0.0 mm/dec to 2.6 mm/dec, for groundnut( rabi) from 0.66 mm/day to 4.45 mm/day and 0.0 mm/dec to 43.9 mm/dec, for groundnut (kharif) from 1.78 mm/day to 4.34 mm/day and 0.0 mm/dec to 14.6 mm/dec, for rice from 0.39 mm/day to 4.82 mm/day and 1.5 mm/dec to 184.9 mm/dec, and for pulses from 2.41 mm/day to 4.22 mm/day and 13.2 mm/dec to 38 mm/dec respectively. The gross water requirement is 939.14 mm/year with an application efficiency of 70%. Therefore the entire land area of 6572 ha requires 61.72 MCM. Thus the dam can conveniently supply the water required for irrigation in the area.
A good water distribution system (WDS) continues to deliver water at all nodes of pipe network to fulfill various water pressure and demand conditions. In this paper, the authors used Genetic Algorithms (GA), a methodology for optimizing pipe networks. A computational code has been developed using Matlab software for optimization of pipe network. To ensure the validity of the code developed, it was tested with the Gessler (1985) pipe network design. This tested code can be further used for design and analysis of pipe network.