Full-Scale Field Implementation of Internally Cured Concrete Pavement Data Analysis for Iowa Pavement Systems

One of the most common curing methods for conventional concrete is external curing. Surface evaporation is a process that dries concrete pavement and decreases the cement hydration rate. External curing keeps the surface moist and mitigates surface drying by evaporation, thus enhancing surface cement hydration. Internal curing is a comparatively recent method to increase the duration of cement hydration by using various internal water reservoirs without changing the water cement ratio and fresh concrete properties in a concrete mixture. This method has been developed mainly to improve concrete durability and decrease shrinkage in concrete structures. In this study, Lightweight Fine Aggregate (LWFA) has been used as internal water reservoirs in new mixtures design applied to two county roads in Iowa. A testing regime including compressive, and tensile strength, modulus of elasticity, surface resistivity, calorimetry, and scanning electron microscopy has been applied to evaluate using LWFA. In the field, a Decagon 5TE sensor was used to measure the relative permittivity and temperature of the concrete materials after construction. The laboratory and field results showed using LWFA improved the mechanical and potential durability properties of concrete and prolong the cement hydration. At the time of its construction, using LWFA in the project was considered and various advantages and disadvantages are evaluated through the Five Dimensions of Project Management framework (5DPM) in comparison with the traditional concrete paving method. Cost and Finance analysis present that the main disadvantage of using LWFA is that it will increase initial costs up to 3.2%, while the main advantage of using LWFA is lowering the total life cycle cost up to 13.2%

Optimal Cultivation Pattern to Increase Revenue and Reduce Water Use: Application of Linear Programming to Arjan Plain in Fars Province

Because the available water resources of the Arjan plain region in Iran do not fully meet the watering requirements for plants in farmlands, the crops suffer from water stress, a situation that causes them to wilt. The aim of this study is to develop a water resources planning model that helps decision-makers determine an appropriate cultivation pattern, optimize the exploitation from surface water resources, and specify the method of allocating water across different farm crops to minimize the detrimental effects of water shortage. Through investigating various models of water resources planning and properties along with the governing conditions for each of these models, the linear programming model was selected as a suitable option due to its simplicity and practical applicability to water resource allocation planning. The model was run for a five-year period by considering gradual variations through the determination of the most appropriate exploitation pattern from the available water resources (surface and groundwater). Results reveal that the negative water balance can be improved gradually as positive, where it will reach +20 million m3 per year in 2040 from the current deficit of 236 million m3 with an 8% increased net profit

Optimal Cultivation Pattern to Increase Revenue and Reduce Water Use: Application of Linear Programming to Arjan Plain in Fars Province

Because the available water resources of the Arjan plain region in Iran do not fully meet the watering requirements for plants in farmlands, the crops suffer from water stress, a situation that causes them to wilt. The aim of this study is to develop a water resources planning model that helps decision-makers determine an appropriate cultivation pattern, optimize the exploitation from surface water resources, and specify the method of allocating water across different farm crops to minimize the detrimental effects of water shortage. Through investigating various models of water resources planning and properties along with the governing conditions for each of these models, the linear programming model was selected as a suitable option due to its simplicity and practical applicability to water resource allocation planning. The model was run for a five-year period by considering gradual variations through the determination of the most appropriate exploitation pattern from the available water resources (surface and groundwater). Results reveal that the negative water balance can be improved gradually as positive, where it will reach +20 million m3 per year in 2040 from the current deficit of 236 million m3 with an 8% increased net profit

Full-Scale Field Implementation of Internally Cured Concrete Pavement Data Analysis for Iowa Pavement Systems

One of the most common curing methods for conventional concrete is external curing. Surface evaporation is a process that dries concrete pavement and decreases the cement hydration rate. External curing keeps the surface moist and mitigates surface drying by evaporation, thus enhancing surface cement hydration. Internal curing is a comparatively recent method to increase the duration of cement hydration by using various internal water reservoirs without changing the water cement ratio and fresh concrete properties in a concrete mixture. This method has been developed mainly to improve concrete durability and decrease shrinkage in concrete structures. In this study, Lightweight Fine Aggregate (LWFA) has been used as internal water reservoirs in new mixtures design applied to two county roads in Iowa. A testing regime including compressive, and tensile strength, modulus of elasticity, surface resistivity, calorimetry, and scanning electron microscopy has been applied to evaluate using LWFA. In the field, a Decagon 5TE sensor was used to measure the relative permittivity and temperature of the concrete materials after construction. The laboratory and field results showed using LWFA improved the mechanical and potential durability properties of concrete and prolong the cement hydration. At the time of its construction, using LWFA in the project was considered and various advantages and disadvantages are evaluated through the Five Dimensions of Project Management framework (5DPM) in comparison with the traditional concrete paving method. Cost and Finance analysis present that the main disadvantage of using LWFA is that it will increase initial costs up to 3.2%, while the main advantage of using LWFA is lowering the total life cycle cost up to 13.2%.</p

Optimal Cultivation Pattern to Increase Revenue and Reduce Water Use: Application of Linear Programming to Arjan Plain in Fars Province

Because the available water resources of the Arjan plain region in Iran do not fully meet the watering requirements for plants in farmlands, the crops suffer from water stress, a situation that causes them to wilt. The aim of this study is to develop a water resources planning model that helps decision-makers determine an appropriate cultivation pattern, optimize the exploitation from surface water resources, and specify the method of allocating water across different farm crops to minimize the detrimental effects of water shortage. Through investigating various models of water resources planning and properties along with the governing conditions for each of these models, the linear programming model was selected as a suitable option due to its simplicity and practical applicability to water resource allocation planning. The model was run for a five-year period by considering gradual variations through the determination of the most appropriate exploitation pattern from the available water resources (surface and groundwater). Results reveal that the negative water balance can be improved gradually as positive, where it will reach +20 million m3 per year in 2040 from the current deficit of 236 million m3 with an 8% increased net profit

Harmful Algae Bloom Prediction Model for Western Lake Erie Using Stepwise Multiple Regression and Genetic Programming

The Great Lakes are most important freshwater bodies providing water resources and other various related businesses to the northeastern part of North America. However, harmful algal blooms (HABs) are more often and severe in those lakes than before and thus threatening lake environments and economies. Researchers have studied the factors influencing HABs characteristics using different scientific methods. In this study, all possible predictors and predictand variables were collected from various data source and then eight final predictors and one predictand were selected based on correlation between predictors and predictand variables. This study tests two machine learning techniques, Stepwise Multiple Regression (SMR) and Genetic Programming (GP), to forecast monthly HAB indicators in Western Lake Erie from July to October. SMR and GP models were created with selected input variables for two training periods, 2002 to 2011 and 2002 to 2014. A Spearman rank correlation coefficient was used to choose input variable sets for each HAB month considering 224 different combinations of lag time and average periods. The SMR models showed a correlation coefficient increase from 0.71 to 0.78 when extending the training period. The GP models followed a similar trend increasing the overall correlation coefficient from 0.82 to 0.96. Both models optimally selected monthly discharge and phosphorus mass from Maumee River Basin as significant predictor variables. A major drawback of both models was data-dependency as common in data-driven methods. GP was better to detect high nonlinear HAB mechanism than SMR due to its nature to use many mathematical functions while SMR only use the linear combination of variables. This study attested that both SMR and GP can be useful to simulate historical HAB event and predict future HAB severity. In future work, to avoid under- or over-prediction for unobserved HAB mechanism regarding short training period, it is suggested to develop an extrapolation technique that is statistically sound and operable in the model and test multi-model ensemble approaches to provide most possible HAB prediction

Optimizing Water Supply through Reservoir Conversion and Storage of Return Flow- A Case Study at Joe Pool Lake

Maintaining an adequate water supply is one of the key challenges faced by the Dallas-Fort Worth Metroplex, where increasing population and rising water demand have elevated the vulnerability of the communities to water shortages. In this paper, we conducted a preliminary study exploring the possibility of converting flood storage in the Joe Pool Lake (JPL) as a means to improve water supply reliability and achieve better cost efficiency. This study employs a mixed integer linear programming (MILP) approach that considers the costs of meeting conversion demand and supply requirements over the northern portion of the Trinity River Basin. It includes trade-offs between capturing and storing natural flow versus return flow from the treatment facilities of the Trinity River Authority (TRA). A set of hypothetical prices and demand figures with the record drought of 1940-1996 considered to test the LP model. The optimal strategy yields expansion of JPL and associated storage-diversion on an annual basis. Also, the outcomes of the analyses suggest that, while the conversion would have a positive impact on water availability, storing the return flow might not produce sufficient cost savings; unless higher prices were imposed on the stored-return flow