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Estimation of reference évapotranspiration (ETO) is vibrantly required for estimating crop water requirement and budgeting irrigation scheduling. The beneficial use of water is of great importance due to shortage issues especially in developing countries like Pakistan. The Food and agricultural organization (FAO) developed a Peneman-Montieth (PM) method which can be globally considered as a standard method for estimation of ETOj but it requires numerous climatic data. Consequently, there is a need to find out the next best suitable method after PM method. The Multi-layer perceptron (MLP), Gene expression programing (GEP) and Radial basis function (RBF) were utilized to calculate ETO values. Monthly meteorological data of six different stations located in arid, semi-arid and humid regions of Pakistan covered from 1980 to 2015. Seventeen input combinations comprise of various climatic variables were developed to evaluate the impact on ETO. Of the available meteorological data, 70% was employed in training while remaining 30% used in testing process. The yielded values of the developed models were compared with the ETO estimated by PM method. The outcome of the study was also applied on some other climatic regions located in USA, New Zealand and China for numerous duration only three climatic parameters, namely, maximum temperature, mean relative humidity and wind velocity had a large positive effect on increasing the accuracy of estimating ETO. By comparing the eight performing indices, MLP among all the powerful predictive modeling techniques can also be considered as the superior alternative to the conventional methods in estimation of ET0. [ABSTRACT FROM AUTHOR]

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In the present study, reactive powder concrete (RPC) was investigated with three different types of single fibers that is, steel fiber (SF), glass fiber (GF), and carbon fiber (CF). Moreover, the effect of hybrid SF‐GF, GF‐CF, and CF‐SF on RPC was also investigated. In case of both single and hybrid fiber‐reinforced RPCs, a constant volume fraction of 2% fiber was used. A plain RPC was also produced that served as a reference/control mix. Studied parameters include compressive strength, modulus of elasticity, peak strains in compression, compression toughness, total energy absorbed in compression, splitting tensile strength, and flexural strength. Results showed that among single fiber‐reinforced RPCs, CF‐RPC performed better than both SF‐ and GF‐RPC in compression. Whereas, single SF‐RPC performed better than GF‐ and CF‐RPC in splitting tensile and flexural strength, single SF‐RPC showed significant softening response compared with single CF and GF‐RPC. CF‐RPC showed comparable performance to that of the SF‐RPC in both tensile and flexural strength. But CF‐RPC showed lower toughness than SF‐RPC. Hybridization of 1%SF and 1%CF yielded maximum overall mechanical performance among both single and hybrid fiber RPCs. Maximum attribution (17–38%) of fibers was toward flexural strength compared to other strength properties. [ABSTRACT FROM AUTHOR]

Estimating the quantitate contribution of climate and land use change is necessary for planning water resources. Tarbela catchment in Pakistan was selected for this study. The Mann Kendall and Pettit test has been used for trend analysis of hydro climatic variables. Original climate elasticity method and improved empirical model of precipitation have been used. The results of trend analysis showed that precipitation and runoff trends decreased and potential evaporation trends increased. The annual rainfall and runoff presented a change point around the years 1999 and 1994 respectively. According to change point analysis, the runoff series was divided into two parts. The period before change point has been regarded as the pre-change period and the period after change point has been regarded as the post change period. According to the original climate elasticity method, the relative contribution of climate change and land use change has been computed as 39.3% and 60.7% respectively. Similarly the improved empirical model of precipitation showed relative contributions of climate change as 41.7% and the same for land use change as 58.3%. To validate the land use change contribution we prepared land use maps. It can be concluded that land use and climate change are responsible for runoff change in Tarbela catchment, and both methods performed well and results are in agreement. [ABSTRACT FROM AUTHOR]

This research intends to explore the sustainable masonry bond formation and interface behaviour of brick masonry bonds with different cement mortar ratios. To test the sustainable behaviour of different brick bonds, different tests were applied to evaluate the performance of the developed five brick masonry structures with the help of four mortar ratios. Following that pattern, the methodologies of a prism triplet test, a bond wrench test, a shear bond test and strength tests for brick masonry were applied. The prism triplet test explained the bonding behaviour of mortar by producing a maximum strength (0.21 MPa) with a 1:3 mix ratio, and the minimum strength (0.095 MPa) with a 1:8 mix ratio. The bond wrench test showed a bond strength of maximum 0.0685 MPa with a mortar ratio of 1:3 and a minimum of 0.035 MPa with a mortar ratio of 1:8. The strength tests for masonry structures expressed that compressive strength (0.786 MPa) and flexural strength (0.352 MPa) were found to be at maximum level with a mortar ratio (1:3) with an English bond formation. For predictions of compressive and flexural strength, artificial neural networks (ANNs) were deployed, and successful predictions of these values along with the relationships between different properties of the material, mortar combinations and bond combinations are presented to complete the exploration of the relationship. This pattern can be helpful for the selection of sustainable brick masonry formations for housing development. [ABSTRACT FROM AUTHOR]

Self-Compacting Concrete (SCC) differs from the normal concrete as it has the basic capacity to consolidate under its own weight. The increased awareness regarding environmental disturbances and its hazardous effects caused by blasting and crushing procedures of stone, it becomes a delicate and obvious issue for construction industry to develop an alternative remedy as material which can reduce the environmental hazards and enable high-performance strength to the concrete, which would make it durable and efficient for work. A growing trend is being established all over the world to use industrial byproducts and domestic wastes as a useful raw material in construction, as it provides an eco-friendly edge to the construction process and especially for concrete. This study aims to enlighten the use and comparative analysis for the performance of concrete with added industrial byproducts such as Ground Granulated Blast Furnace Slag (GGBFS), Silica fumes (SF) and Marble Powder (MP) in the preparation of SCC. This paper deals with the prediction of mechanical properties (i.e., compressive, tensile and flexural Strength) of self-compacting concrete by considering four major factors such as type of additive, percentage additive replaced, curing days and temperature using Artificial Neural Networks (ANNs). [ABSTRACT FROM AUTHOR]

Considering network topologies and structures of the artificial neural network (ANN) used in the field of hydrology, one can categorize them into two different generic types: feedforward and feedback (recurrent) networks. Different types of feedforward and recurrent ANNs are available, but multilayer perceptron type of feedforward ANN is most commonly used in hydrology for the development of wavelet coupled neural network (WNN) models. This study is conducted to compare performance of the various wavelet based feedforward artificial neural network (ANN) models. The feedforward ANN types used in the study include the multilayer perceptron neural network (MLPNN), generalized feedforward neural network (GFFNN), radial basis function neural network (RBFNN), modular neural network (MNN) and neuro-fuzzy neural network (NFNN) models. The rainfall-runoff data of four catchments located in different hydro-climatic regions of the world is used in the study. The discrete wavelet transformation (DWT) is used in the present study to decompose input rainfall data using db8 wavelet function. A total of 220 models are developed in this study to evaluate the performance of various feedforward neural network models. Performance of the developed WNN models is compared with their counterpart simple models developed without applying wavelet transformation (WT). The results of the study are further compared with - multiple linear regression (MLR) model which suggest that the WNN models outperformed their counterpart simple models. The hybrid wavelet models developed using MLPNN, the GFFNN and the MNN models performed best among the six selected data driven models explored in the study. Moreover, performance of the three best models is found to be similar and thus the hybrid wavelet GFFNN and the MNN models can be considered as an alternative to the most commonly used hybrid WNN models developed using MLPNN. The study further reveals that the wavelet coupled models outperformed their counterpart simple models only with the parsimonious input vector. [ABSTRACT FROM AUTHOR]

The rainfall-runoff modelling being a stochastic process in nature is dependent on various climatological variables and catchment characteristics and therefore numerous hydrological models have been developed to simulate this complex process. One approach to modelling this complex non-linear rainfall-runoff process is to combine the outputs of various models to get more accurate and reliable results. This multi-model combination approach relies on the fact that various models capture different features of the data, and hence combination of these features would yield better result. This study for the first time presented a novel wavelet based combination approach for estimating combined runoff The simulated daily output (Runoff) of five selected conventional rainfall-runoff models from seven different catchments located in different parts of the world was used in current study for estimating combined runoff for each time period. Five selected rainfall-runoff models used in this study included four data driven models, namely, the simple linear model, the linear perturbation model, the linearly varying variable gain factor model, the constrained linear systems with a single threshold and one conceptual model, namely, the soil moisture accounting and routing model. The multilayer perceptron neural network method was used to develop combined wavelet coupled models to evaluate the effect of wavelet transformation (WT). The performance of the developed wavelet coupled combination models was compared with their counterpart simple combination models developed without WT. It was concluded that the presented wavelet coupled combination approach outperformed the existing approaches of combining different models without applying input WT. The study also recommended that different models in a combination approach should be selected on the basis of their individual performance. [ABSTRACT FROM AUTHOR]

An investigation of the effect of baffles on retention pond performance using a physical model of an existing sediment retention pond is presented. Analysis of residence time (RTD curves) was used to compare the hydraulic performance of different arrangements of baffles in the pond. Five different arrangements for the design of baffles were studied. The results show that placing a single baffle to deflect the influent to a sediment retention pond does not improve pond performance; rather, it stimulates short-circuiting. This is contradictory to the literature and is considered to be a consequence of the model pond incorporating sloping walls, which is a novel aspect of this study. Most of the previous studies have neglected the effects of battered walls. Conversely, the inclusion of more than two baffles was found to increase the hydraulic performance. The results reported here are limited to small and narrow ponds where a large portion of the pond is batter (i.e. made up of sloping walls). For large area ponds, batter effects may be negligible and are likely to be different from those reported here.