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In the present work the development and application of an optimal design process for reinforced concrete frames is exposed, taking on account optimal distributions of reinforcing bars in the structural elements under free-clash and slap reinforcement criteria, aside of other restriction by code specifications (ACI-318 and NTC-17) such as minimum separation and maximum reinforcement area, through visual programming in ANSYS Space-Claim using Python language for the automatic generation of detail CAD drawings. Artificial Intelligence is employed with the meta-heuristic Particle Swarm Optimization algorithm (PSO) given its rapid convergence to find optimal dimensions of the elements for a structural frame, and the Steepest Gradient Descent Method (SGD) was used along with the Idealized Smeared Reinforcement analogy (ISR) to find optimal distributions of rebar in such elements through simple search, considering symmetric reinforcement in columns. Only one numerical structural model was built for experimentation under two different design approaches for comparison: one under free-clash and slap reinforcement criteria and another without considering such criteria. Results show a great influence in the optima convergence of dimensions by considering such reinforcement criteria, Besides, great final visual detailing results of such reinforcement are obtained, demonstrating thus, that it is possible to generate with the technology nowadays available more accurate models for civil infrastructure that may represent better construction conditions under which structures are subject to.

The present work exposes the design of optimization procedures both with the “Particle Swarm Optimization” (PSO) algorithm and the “Genetic Algorithm” (GA) for the design of reinforced concrete frames, making comparisons in cost, weight of the structure and predicted damage. The optimization procedures are built up using the “Idealized Smeared Reinforcement” (ISR) analogy for each element of the structural model frames considered for this work.Two different numerical structural plane-frame models were created for the application and comparison of the performance of the optimization design procedures hereby proposed. The optimization procedures were mono-objective with a cost-objective function, taking on account steel reinforcement and concrete for the cost computation. Two different design approaches were carried on for this work, one proposing asymmetrical reinforcement for columns and the other with symmetrical reinforcement. In order to compute the damage indices considered for this study a non-linear Pushover structural analysis is performed.Results show that asymmetrical reinforcement in columns may reduce concrete volumes, although such reduction in material might not be quite proportional with construction cost, given that asymmetric reinforcement in columns is more expensive than symmetrical, per unit-cost. The bigger the structure, the more likely is to obtain lighter structures by using asymmetrical reinforcement. Regarding damage of the structure, results show that when using asymmetrical reinforcement in columns, it is more likely to obtain smaller values for the expected damage with no great difference on the estimated collapse Safety Factors for the seismic loads. In general, the proposed methodology hereby proposed enhances quite good optima results, requiring only a few adjustments of clash-free and slap reinforcement after the optimization procedure terminates.When designing reinforced concrete frames with asymmetric reinforcement in columns, an increase in construction costs of as much as $25\%$ as that obtained for symmetric reinforcement could be enhanced. In general, with the proposed methodology to optimally design reinforced concrete frames, savings of as much as $20\%$ in construction costs from an initial structural proposal can be reached.

The dual-purpose bovine production system (DP) is the most widespread small-scale model in Latin American tropics, where it constitutes a key tool in terms of food security. Most DPs are subsistence farms oriented to self-consumption, with a very low technology adoption rate. Hence, the main challenge is how to improve the technological level without compromising the system sustainability by applying land-sharing practices. Thus, through networks methodology, this paper analysed how farmers adopt reproductive technologies. The sample consisted of 383 very small farms of dual-purpose cattle. Seven reproduction technologies oriented to improve reproductive efficiency were evaluated: Breeding soundness evaluation in bulls, semen fertility evaluation, evaluation of female body condition, oestrus detection, pregnancy diagnosis, seasonal or continuous mating, and breeding policy. Social Network Analysis (SNA) allowed identifying adoption patterns, as the joint adoption of semen fertility evaluation, estrus detection, and pregnancy diagnosis, which were consider complementary technologies. Similarly, breeding soundness evaluation in bulls was found to be the most widely adopted technology. The results showed that these farmers presented a very low level of reproduction technology adoption rate and suggested that farmer’s affiliation with organizations such as the Livestock Groups for Technological Validation and Transfer (GGAVATT), and its network position had a significant impact on the level of technological adoption. In the first stage of adoption, this work highlighted the importance of centralized models from the GGAVATT to the farmers, related to the knowledge and absorption dynamic capabilities. In a later stage, decentralized models through technological leaders are a priority, related to integration and innovation dynamic capabilities.

Technological level of double purpose bovine farms in the Ecuadorian Coast Area is provided. The effect of flock size and ecological area over results was evaluated. A random sample of 44 farms was collected and a survey composed by 125 questions was applied. As main classification factors, flock size, according to the number of cows (small and medium size) and ecological area (dry and tropical forests) were considered. 38 technologies were included and classified by means of an expert panel in six technological areas. Later, the level of innovation for each region was calculated according to the criteria of having implemented or not the technology (1 and 0). The comparison of multiple samples was used to classify areas and after that, a general lineal model containing two factors is applied (GLM). The level of technological innovation was a 51%, where the areas of animal feeding, reproduction-genetics and health are the more advance ones. There was an outstanding impact of size and ecological area over the global technological level. This way the smallest farms were the ones located in the dry tropical forest and present lower levels of technological adoption. On the contrary, medium size farms located in the wet forest showed higher technological levels. The improvement in the technological level of implementation requires of multiple actions; amongst others, those related to managerial aspects as the individual animal identification and the use of productive records. A strategic approach must be followed; for the development of reproductive plans and prevention, the fight and control of diseases. Besides, the improvement of the equipment requires of basic actions as the building of stockyards, the improvement of the milking parlor among others. Se determinó el nivel tecnológico de las explotaciones bovinas de doble propósito de la Costa Ecuatoriana y se evaluó el efecto del tamaño del rebaño y la zona ecológica. Se tomó una muestra aleatoria de 44 explotaciones a las que se les aplicó un cuestionario de 125 preguntas. Se establecieron como factores de clasificación el tamaño del rebaño, (explotaciones pequeñas y medianas) y la zona ecológica (bosque seco y bosque húmedo tropical). Se identificaron 38 tecnologías que se clasificaron mediante un panel de expertos en seis áreas tecnológicas. Posteriormente se calculó el nivel de innovación para cada área según su nivel de adopción tecnológica. Se utilizó la comparación de muestras múltiples para clasificar las áreas y a continuación se aplicó un modelo lineal general (GLM) con dos factores. El nivel de innovación tecnológica fue del 51% y las áreas alimentación, reproducción-genética y salud animal fueron las más avanzadas. Existe un marcado efecto de la dimensión y la zona ecológica sobre el nivel tecnológico global, de modo que las explotaciones de menor dimensión y las del bosque seco muestran menor nivel tecnológico. Por el contrario, las explotaciones de mediana dimensión y situadas en el bosque húmedo muestran mayor nivel tecnológico. La mejora del nivel tecnológico precisa de múltiples acciones; en el área de gestión: la identificación individual de los animales y la utilización de registros productivos. El asesoramiento es estratégico; tanto para el desarrollo de planes reproductivos como de prevención, lucha y control de las enfermedades. Por otra parte, la mejora del equipamiento precisa de acciones básicas como la instalación de corrales, la mejora de la sala de ordeño, entre otros. 164-172 rivasjoseh@gmail.com guisell2010@hotmail.com pa2pemuj@uco.es