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Propue= sta de modelo matemático para calcular el rendimiento de mando de obra en mampostería de bloque. Caso: ciudad de Cuenca, parroquia Cañaribamba=

 <= /p>

Proposal of a mathematical model to calculate the performance of work command in block masonry. case: Cuenca city, Cañaribamba Parish

 <= /p>

1

Micaela Geovanna Coronel García

https://orcid.org/0009-0004-86= 52-0329

 

Maestría en Construcción con mención en Administ= ración de la Construcción Sustentable, Universidad Católica de Cuenca, Cuenca, Ecuador.

micaela.coronel.26@est.ucacue.edu.ec

2

Carlos Julio Calle Castro

https://orcid.org/0000-0002-68= 91-0030  

 

 Maestrí= a en Construcción con Mención en Administración de la Construcción Sustentable, Universidad Católica de Cuenca, Cuenca, Ecuador.=

@ucacue.edu.ec

= 2=

Marco Ávi= la Calle

https://orcid.org/0000-0002-2134-1432

 <= /span>

Maestría en Construcción con mención en Administración de la Construcción Sustenta= ble, Universidad Católica de Cuenca, Cuenca, Ecuador..

mavila@ucacue.edu.ec

 

&= nbsp;

 

Artículo de Investigación Científica y Tecnológica

Enviado: 15/12/2023

Revisado: 18/01/2024

Aceptado: 07/02/2024

Publicado:05/03/2024

DOI: https://doi.org/10.33262/c= oncienciadigital.v7i1.3.2938        =

 

 

 

Cítese: <= /b>

 

 

Coronel García,= M. G., Calle Castro, C. J., & Ávila Calle, M. (2024). Propuesta de modelo matemático para calcular el rendimiento de mando de obra en mampostería de bloque. Caso: ciudad de Cuenca, parroquia Cañaribamba. ConcienciaDigital, 7(1.3), 49-68. https://d= oi.org/10.33262/concienciadigital.v7i1.3.2938

 

 

 

CONCIENCIA DIGITAL, es una revista multidisciplinar, <= b>trimestral, que se publicará en soporte electrónico tiene como misión contribu= ir a la   formación de profesionales competentes con visión humaníst= ica y crítica que sean capaces de exponer sus resultados investigativos y científicos en la misma medida que se promueva mediante su intervención cambios positivos en la sociedad. https://concienc= iadigital.org  

La revista es editada por la Editorial Ciencia Digital (Editorial de prestigio registra= da en la Cámara Ecuatoriana de Libro con No de Afiliación 663) www.celibro.org.ec

 

 

 

Esta revista es= tá protegida bajo una licencia Creative Commons AttributionNonCommercialNoDerivatives 4.0 International. Copia de la licencia: http://creativec= ommons.org/licenses/by-nc-nd/4.0/

 

Palabras claves: Rendimiento, regresión lineal, mano de obra, mampostería.

 

Resumen

Introducción.  La colocación de la mampostería de bloque emerge como una fase crí= tica en el proceso constructivo, donde la eficiencia y precisión influyen directamente en la duración y calidad del proyecto. Esta actividad, aunque aparentemente simple, conlleva una complejidad inherente que a menudo res= ulta en retrasos significativos en la ejecución de la obra. La necesidad de comprender y abordar los factores que contribuyen a estos retrasos es evidente, ya que su impacto no solo se refleja en términos de cronograma = y presupuesto, sino también en la satisfacción del cliente. Objetivo. Proponer un modelo matemático para calcular el rendimiento de la mano de obra en la colocación de mampostería con bloques en la Parroquia Cañaribamba, Cuenca, Ecuador. Metodología. El diseño metodológico adoptado sigue una orientación relacional y descriptiva, involucrando la recopilación de dat= os de nueve obras mediante una ficha de observación que abarca tanto factores externos como internos. Utilizando estos datos, se llevó a cabo un anális= is de regresión lineal mediante un programa estadístico. Resultados. = Los resultados destacan que, individualmente, ningún factor analizado influye significativamente en el rendimiento laboral; sin embargo, la combinación= de estos factores permite prever el rendimiento con una precisión del 93.3%.= Conclusión.  Se concluye que la regresión line= al emerge como una herramienta robusta para anticipar el rendimiento de cuadrillas de obreros en la Parroquia Cañaribamba, considerando la complejidad de factores tanto internos como externos en las obras. Área de estudio general: Ingeniería, Industria y Construcción Área de estudio específica: Administració= n de la Construcción

 

 

Keywords:

Performance, linear regression, labor, masonry.

 

Abstract=

Introduct= ion.  The placement of block masonry emerges as a crit= ical phase in the construction process, where efficiency and accuracy directly influence the duration and quality of the project. This activity, although seemingly simple, carries an inherent complexity that often results in significant delays in the execution of the work. The need to understand a= nd address the factors contributing to these delays is evident, as their imp= act is not only reflected in terms of schedule and budget, but also in client satisfaction. Objective.  = To propose a mathematical model to calculate the labor performance in the placement of masonry with blocks in Cañaribamba Parish, Cuenca, Ecuador. = Methodology.  The methodological design adopted follows a relational and descriptive orientation, involving the collectio= n of data from nine construction sites by means of an observation sheet that covers both external and internal factors. Using these data, a linear regression analysis was carried out using a statistical program. Resul= ts.  The results highlight that, individual= ly, none of the factors analyzed significantly influences job performance; however, the combination of these factors allows predicting performance w= ith an accuracy of 93.3%. Conclusion.  It is concluded that linear regression emerges as a robust tool to anticipate the performance of work crews in the Cañaribamba Parish, considering the complexity of both internal and external factors in the works.=

 

 

 

 

Introducción

En la actualidad, el incremento de la competenci= a y las demandas cada vez más rigurosas en términos de calidad por parte de los consumidores han impulsado una evolución significativa en la gestión de proyectos constructivos (Jeremiah et al., 2019). Este desarrollo va más all= á de simplemente lograr una ejecución de alta calidad, en la era moderna, la optimización de recursos se ha convertido en una prioridad para gerentes y administradores con el fin de generar valor y al mismo tiempo mitigar pérdi= das potenciales en áreas clave como: tiempo, financiamiento y planificación en = la industria de la construcción (Atencio et al., 2022).

Desde esta perspectiva, entre las herramientas esenciales a disposición de los administradores y gestores de construcción = para la planificación y estimación de costos de operación, se encuentra el cálcu= lo del rendimiento de la mano de obra. Este factor se establece como un determinante clave para lograr resultados gerenciales efectivos, asimismo, = una precisa anticipación del rendimiento laboral es de suma importancia, ya que= su aplicación incorrecta puede acarrear consecuencias desfavorables en término= s de planificación, presupuesto, control de calidad y satisfacción del cliente (Ouyang et al., 2022).

En el mismo orden de ideas, en la industria de la construcción, existen varias actividades que se pueden considerar como: Actividades Críticas (AC), es decir, que tienen el potencial de generar gra= ndes retrasos en obra si no son correctamente planificados y ejecutados por pers= onal eficiente (Yap et al., 2021). Entre estas AC se encuentra la colocación de mampostería de bloque que puede llegar a perjudicar los tiempos de ejecució= n de manera significativa si no son correctamente planificados (Xu et al., 2021)= . En el escenario dado, surgen algunas preguntas importantes: ¿Cuáles son los factores que ejercen un impacto perjudicial en el desempeño laboral de quie= nes intervienen en la colocación de mampostería de bloques? y ¿Es posible prede= cir el rendimiento de la mano de obra en la actividad de colocación de mamposte= ría de bloque a través de un modelo matemático?

El objetivo principal de este estudio, es propon= er un modelo matemático basado en la Regresión Lineal (RL), a través de la aplicación de instrumentos de recolección de datos en diferentes obras en la ciudad de Cuenca para identificar los factores que influyen de manera direc= ta en el rendimiento de la mano de obra. La importancia de alcanzar este objet= ivo reside en su aporte al conocimiento integral dentro del ámbito de la construcción, particularmente en lo que respecta a los factores que influyen decisivamente en el desempeño de la mano de obra que se dedica a la colocac= ión de bloques en la ciudad de Cuenca.

En este contexto, la utilización de una herramie= nta predictiva que incorpore los criterios antes mencionados conducirá a una me= jor planificación de recursos y una mayor precisión en la proyección de los pla= zos de finalización del proyecto. El potencial de predecir el desempeño laboral utilizando estas características brinda a los gerentes la oportunidad de refinar sus estrategias con mayor precisión, reduciendo así posibles retras= os y maximizando la asignación de personas y recursos materiales. Es decir, el l= ogro del objetivo planteado en esta investigación no sólo mejorará la progresión= del conocimiento dentro del sector de la construcción, también proporcionará a = los administradores un valioso instrumento para tomar decisiones bien informada= s y formular planes estratégicos, maximizando así los resultados.

Esta investigación se llevó a cabo en la ciudad = de Cuenca, específicamente en la Parroquia Cañaribamba, un sector que tiene las condiciones ideales para el cumplimiento de los objetivos de esta investiga= ción ya que se considera como un centro de expansión urbana continua y tiene var= ios proyectos de construcción en curso que incluyen a la colocación de mamposte= ría de bloques como un eje central en la mayoría de proyectos.

La investigación se lleva a cabo en el contexto = de nueve proyectos arquitectónicos distintos en la Parroquia Cañaribamba donde= se están llevando a cabo las etapas iniciales de instalación de mampostería de bloques. Los proyectos antes mencionados fueron escogidos por estar ubicado= s en la zona de estudio en la fase de colocación de mampostería. La metodología = de investigación empleada en este estudio es una combinación de líneas relacionales y descriptivas con un enfoque cuantitativo, que incorpora anál= isis estadístico y técnicas de regresión lineal. Este enfoque permitió identific= ar tendencias entre variables cruciales y desarrollar modelos de predicción y recomendaciones destinadas a estandarizar y mejorar la eficiencia en los proyectos de construcción.

La Variable Dependiente (VD) de esta investigaci= ón es el Rendimiento de Mano de Obra (RMO) que se puede conceptualizar como el volumen de trabajo sobre unidad de tiempo (m2/hora) se obtiene mediante la recolección de datos obtenidos tanto de los trabajadores como de fuentes gubernamentales. Por otro lado, las Variables Independientes (VI) de esta investigación, son los factores que pueden influenciar en el RMO y fueron determinados a partir de la investigación de Cano y Duque (2000) tales como= : el clima, tipo de actividad, equipamiento, supervisión y condiciones propias d= el trabajador. Los datos para la definición de las VI se determinarán a través= de fichas de observación. El análisis de la interacción entre estas variables proporcionará información sobre los resultados de la colocación de bloques = de mampostería en términos de rendimiento.

En el marco de lo expuesto, la presente investigación pretende calcular el rendimiento estándar en la colocación de= la mampostería de bloque, para esto se realizaron análisis de correlación y se utilizó el método de regresión lineal para predecir o estimar dicho rendimi= ento en función de diferentes factores o variables explicativas. Se buscó proporcionar una herramienta que ayude a evaluar las probabilidades de alca= nzar un alto rendimiento en los obreros en función de las variables independient= es que afecta en el rendimiento de mano de obra de la colocación de la mampost= ería de bloque.

A continuación, se presenta el material teórico referencial que se utilizó para la elaboración de este documento que permit= ió caracterizar las variables bajo estudio:

Rendimiento de mano de obra

El RMO, se refiere a la capacidad de un trabajad= or o equipo para completar tareas en un período de tiempo específico. Esta medid= a, que frecuentemente se expresa en términos de producción por hora, día o sem= ana, sirve como una evaluación básica de la efectividad y productividad de la fu= erza laboral. Este indicador es más que un simple número; es fundamental para evaluar el desempeño y mejorar los procesos de trabajo. Al cuantificar la relación entre el trabajo completado y el tiempo invertido, las empresas pu= eden identificar áreas de fortaleza y oportunidades de optimización. Esto mejora= la gestión de recursos y da como resultado mejores rendimientos laborales (Coc= k et al., 2022).

A lo mencionado, calcular el RMO es importante e= n la construcción por diversas razones. La primera ventaja es que permite a los directores de proyectos predecir cuánto trabajo pueden completar en un perí= odo de tiempo determinado, lo que facilita la planificación y programación del proyecto. En segundo lugar, al utilizar el cálculo del RMO para identificar cuellos de botella y puntos problemáticos en el proceso de construcción, los administradores pueden tomar medidas para aumentar la productividad y la eficiencia. En tercer lugar, el cálculo del RMO puede ayudar a los gerentes= a establecer metas alcanzables para el equipo de trabajo, aumentando el compromiso y la motivación de los empleados (Bartoschek y Kamenov, 2021). <= o:p>

Comprender los diversos factores que influyen en= el RMO de la construcción tiene una importancia significativa, ya que permite a los gerentes de proyectos reconocer y resolver de manera efectiva cualquier inquietud que pueda estar impidiendo la productividad de los trabajadores. A través de una comprensión integral de los diversos factores que ejercen inf= luencia en la productividad laboral, los gerentes de proyectos están capacitados pa= ra implementar medidas destinadas a mejorar la eficiencia de los trabajadores. Esto, a su vez, tiene el potencial de generar una reducción de costos y una elevación de la calidad general del trabajo. Además, mejorar el RMO puede conducir a una mayor ventaja competitiva para la empresa dentro del mercado= y a una elevación de la satisfacción del cliente (Hamza et al., 2022).

Pero, ¿Cómo saber qué factores influencian el RM= O? Para responder a este cuestionamiento, se puede utilizar algunos métodos de identificación de factores influyentes disponibles. En el caso de esta investigación se usa los criterios proporcionados por Cano y Duque (2000) quienes menciona dentro de su investigación que se puede determinar los factores anteriormente mencionados a través de 7 categorías que son: <= /o:p>

1. En el ámbito de la economía existen diversos factores que influyen en la productividad. Estos factores abarcan la inflac= ión, las tasas de interés, la disponibilidad de materiales y la competencia del mercado. 2. Dimensiones laborales: elementos relacionados con las condicion= es de trabajo, abarcando aspectos como remuneración, seguridad social, capacit= ación y motivación. 3. El clima se refiere al conjunto de elementos ambientales q= ue pueden afectar el desempeño de los trabajadores, abarcando factores como la temperatura, la humedad, las precipitaciones y la radiación solar. 4. Actividad: Factores relacionados con la actividad a realizar, incluido el n= ivel de dificultad, complejidad, duración e interconexión con otras actividades.= 5. Equipo: Factores relativos a los equipos y herramientas empleados, abarcando aspectos como calidad, estado, suficiencia, mantenimiento y accesibilidad. = 6. Supervisión: Este criterio abarca varios factores asociados con la supervis= ión y orientación brindada a las personas en su trabajo, incluida la calidad, cantidad, oportunidad y eficacia de la supervisión. 7. Los factores pertene= cientes al trabajador abarcan varios aspectos, incluyendo su experiencia, nivel de habilidad, estado de salud, motivación y actitud.<= /p>

 

 

Regresión lineal

La RL es una metodología estadística empleada pa= ra establecer un modelo matemático que representa la asociación entre una VD y= una o más VI. El propósito de este método es hacer predicciones sobre el valor = de la VD utilizando los valores de las VI. La RL se basa en el supuesto de que existe una asociación lineal entre las variables, lo que implica que las alteraciones en las VI van acompañadas de modificaciones proporcionales en = la VD. El objetivo de la RL es identificar la línea de regresión óptima que minimice la diferencia entre los valores observados y los valores predichos= . La línea de mejor ajuste se determina mediante la aplicación del método de mín= imos cuadrados, cuyo objetivo es minimizar la suma de las diferencias al cuadrado entre los valores observados y los valores predichos (Maulud y Mohsin, 2020= ).

La utilización de la RL es un enfoque competente para evaluar la correlación entre una VD, como la productividad laboral, y múltiples variables independientes, como los factores que influyen en la productividad laboral. La utilización del análisis de regresión múltiple permite evaluar el impacto de VI individuales sobre la VD, al tiempo que ti= ene en cuenta la influencia potencial de otras variables independientes. Los coeficientes de regresión estandarizados se emplean para determinar el grad= o de influencia que cada factor ejerce sobre la productividad laboral (Hai y Tam, 2019).

La RL permite a los administradores hacer predicciones sobre valores de precisión futuros mediante la creación de un modelo de la relación entre variables. La importancia de esto radica en su capacidad para permitir a las organizaciones tomar decisiones bien informad= as con respecto a la mejora de su eficacia gerencial. Por ejemplo, en el caso = de que el análisis de regresión lineal indique una asociación significativa en= tre la precisión de la entrega de un proyecto y la gestión de objetivos, una organización puede implementar medidas para mejorar sus procedimientos de gestión de objetivos, lo que en consecuencia conducirá a una mejora en la precisión de la entrega de sus objetivos (Gata et al., 2019).

Metodología

Diseño

La presente investigación sigue un diseño metodoló= gico enmarcado en la línea de investigación de tipo relacional - descriptivo que= se centra en establecer patrones entre variables clave que afectan el rendimie= nto de la mano de obra en la colocación de mampostería de bloque, esto se desarrolló a través de análisis estadísticos basados en la regresión lineal, donde se pudo establecer modelos predictivos y recomendaciones para la predicción del posible rendimiento que se puede obtener de los obreros y la mejora de la eficiencia en este ámbito. Existen dos enfoques del problema: = El primero; es el análisis descriptivo mediante el cual se estudian las causas= o factores que afectan al rendimiento real de mamposterías de bloque y las posibles situaciones que las potencian. El segundo; es el análisis relacion= al, objeto de estudio de este trabajo, cuya finalidad es analizar la correlación existente entre las VI y sus indicadores

El enfoque metodológico que se utilizó es el cuantitativo, puesto que los datos que se van a recolectar de las obras, así como su posterior análisis se van a realizar a través de un programa estadístico de información que permitió obtener relaciones numéricas entre = las variables. Esta investigación utiliza 4 etapas metodológicas que se mencion= an a continuación:

Definición de variables

Para realizar la recopilación de datos es importan= te iniciar con la operacionalización de las variables de estudio, de esta mane= ra se puede definir de manera específica qué se pretende medir y como se planea realizar estas mediciones. En el caso específico de esta investigación, se = mide una VD que es el RMO a través de diferentes VI identificados en la referenciación teórica y sus indicadores que están representados con una transposición numérica (valores equivalentes desde el 1-5) que se presentan= a continuación en la tabla 1.

Tabla 1

O= peracionalización de variables

VI

Indicadores

1

2

3

4

5

Clima

Tiempo

Tormenta

Aguacero

Llovizna

Nublado

Despejado

Temperatura

Muy Caluroso/MuyFrio

N/A

Caluroso/Frio

N/A

Fresco

Suelo

Pantanero

Charcos

Piso húmedo

Piso seco

Piso duro

Cubierta

Sol

N/A

Normal

N/A

Sombra

Actividad

Dificultad

Difícil

N/A

Normal

N/A

Fácil

Peligro

Peligrosa

Riesgosa

Normal

Moderado

Ningún peligro

Interrupciones

≥ 1 hora

15≥60 min

5≥15 min

0≥5 min

Ninguna

Orden y aseo

Difícil acceso

Escombro

Transitable

Poca sucidad

Aseo total y orden

Actividades precedentes

Repetir

Mucho resane

Poco resane

Aceptable

Perfecta

Tipicidad

De 1 a 5

De 5 a 10

De 10 a 15

De 15 a 20

Más de 20

Tajo (Espacio de trabajo)

Muy estrecho

Estrecho

Normal

Amplio

Muy amplio

Equipamiento

Herramienta

Inadecuada

N/A

Adecuada

N/A

Especial

Equipo

Inadecuada

N/A

Adecuada

N/A

Especial

Mantenimiento

Nulo

N/A

Aceptable

N/A

Bueno

Suministro

Nunca

N/A

A veces

N/A

Siempre

Elemento de protección

Ninguno

N/A

Casi todos

N/A

Todos

Tabla 1

Operacionalización de variables (contin= uación)

VI

Indicadores

1

2

3

4

5

Clima

Tiempo

Tormenta

Aguacero

Llovizna

Nublado

Despejado

Temperatura

Muy Caluroso/MuyFrio

N/A

Caluroso/Frio

N/A

Fresco

Supervisión

Dirección (criterios de aceptación)

Ninguno

Informales

Verbales

Verbales previos

Bajo escrito

Instrucción

Ninguna

N/A

Verbal - requerida

N/A

Documento requerido

Seguimiento

Sin revisión

N/A

Revisión eventual

N/A

Siempre

Supervisor (Maestro)

Malo

N/A

Regular

N/A

Bueno

Aseguraiento de Calidad

No existe

Esfuerzos aislados

Inventoría

En proceso

Certificado ISO

Trabajador

Situación personal

Neurótico

Triste

Normal

Buena

Excelente

Ritmo de trabajo

Lento

N/A

Normal

N/A

Rápido

Salud

Enfermo

N/A

Normal

N/A

Excelente

Habilidad

Inexperto

N/A

Hábil

N/A

Experto

Capacitación

Ninguna

Aprendiz

Requerida

Experto

Certificado

Laborales

Contrato

Administración

N/A

N/A

N/A

Subcontratación

Sindicato

Si

N/A

N/A

N/A

No

Incentivos

No

N/A

N/A

N/A

Si

Salario

SMLV

N/A

N/A

N/A

≥SMLV

Seguridad social

Si

N/A

N/A

N/A

No

Nota. Adaptado de Cano y Duque (2000)

Elaborado por: Autores

Diseño de formulario de recolección de datos =

Se creó una ficha de observación que fue aplicada en nue= ve obras distintas que permitió recopilar información sobre las variables identificadas en la operacionalización de las variables. La ficha de observación estuvo conformada por 26 opciones de respuesta que están alinea= das con los indicadores de cada VI.

Definición de universo y muestra <= /span>

El universo de estudio de esta investigación se enfoca en los proyectos arquitectónicos en la fase de colocación de mampostería en Cuenca, Ecuador, con la población objetivo constituida por las 21 construcciones en ejecución ubicadas en la parroquia Cañaribamba. La determinación de la muestra se llevó a cabo mediante la aplicación de la técnica de muestreo por conveniencia, la cual implica la selección no aleat= oria de elementos basada en la accesibilidad y conveniencia del investigador, en contraposición a un método de selección aleatoria más riguroso (Hernández, 2021). En este contexto, se eligió una muestra de seis proyectos arquitectónicos en proceso de colocación de mampostería, excluyendo aquello= s en los que dicha fase ya había sido concluida. Para la recolección de datos de= los obreros en esta muestra se utilizó a la totalidad de la mano de obra, es de= cir: 49 obreros.

Análisis de los resultados

El análisis de los datos se inició con pruebas de normalidad para= cada factor, utilizando un programa estadístico que aplicó la prueba de Shapiro-= Wilk (SW). Esta prueba proporcionó un valor P, el cual indica la homogeneidad de= los datos y se interpreta según un umbral estándar de 0.05. Si el valor P resultante de la prueba SW es superior a 0.05, se considera que el factor s= igue una distribución normal. Por otro lado, si el valor P es menor a este umbra= l, se concluye que los datos no cumplen con los criterios de normalidad.

Tras el análisis de normalidad, se optó por un método de correlac= ión entre los distintos factores presentes en la tabla 1 y el rendimiento de los obreros en cada obra. En este caso particular, se empleó el análisis de Kruskal-Wallis (KW), el cual no requiere supuestos de normalidad para establecer correlaciones. El análisis de KW evalúa el nivel de asociación a través de la prueba de chi-cuadrado (χ²), los grados de libertad (gl) = y el nivel de significancia (p), proporcionando así una medida robusta de la relación entre variables sin depender de la distribución de los datos.

Tras el análisis correlacional, se procede con la construcción del modelo matemático basado en una regresión lineal múltiple. Esta regresión se divide en dos etapas distintas. La primera etapa involucra la utilización de medidas de ajuste que revelan la efectividad del modelo. Esto se logra medi= ante el coeficiente de correlación lineal (R) en conjunto con el coeficiente de determinación (R²), los cuales indican la fuerza y representatividad de las variables en el modelo.

Además, se han incorporado al análisis métricas esenciales como e= l AIC (Criterio de información de Akaike), el BIC (Criterio de información bayesi= ano) y el RMSE (Error cuadrático medio). El AIC y el BIC tienen funciones distin= tas: facilitan la comparación de modelos al tener en cuenta tanto la bondad del ajuste como la complejidad, mientras que el RMSE cuantifica la precisión del modelo midiendo las discrepancias entre los valores observados y anticipado= s.

En la segunda etapa de la regresión lineal, se calculan los coeficientes del modelo. Estos coeficientes se pueden clasificar en tres ti= pos principales, uno de los cuales es el Coeficiente Estimador (CE) que represe= nta el vínculo entre las variables independientes y dependientes. El coeficient= e EE cuantifica la precisión de esta estimación, mientras que el coeficiente t evalúa el nivel de desviación de este valor respecto de cero. El coeficient= e p cuantifica la probabilidad de observar esta conexión si no existe una relac= ión genuina entre las variables

Resu= ltados 

La primera prueba utilizada en los datos recolecta= dos fue Shapiro-Wilk para comprobar supuestos de normalidad, evidenciando valor= es p≤ 0.001 en todos los conjuntos de datos de los factores, por lo cual= , no se tienen certeza o evidencia estadística que la información siga una distribución y homogeneidad normal. Ya que, la distribución no cumple con criterios de normalidad para realizar la correlación se usó el análisis de Kruskal-Wallis para los grupos de datos. Estos resultados se muestran en la tabla 2.

El análisis de Kruskal-Wallis arrojó hallazgos estadísticamente significativos para varios factores que impactan el desemp= eño laboral de los trabajadores. Los datos analizados demuestran una notable consistencia en su importancia estadística, como lo indican los valores altamente significativos de Chi-cuadrado (χ²) (p <.001) en todas las categorías estudiadas. Varios factores, incluidas variables físicas como la temperatura, el suelo y la cobertura, así como elementos relacionados con el entorno laboral, como la gestión, la capacitación y la supervisión, impacta= ron significativamente la eficiencia del trabajo. Este análisis examina la rela= ción entre diversos factores y la productividad en las obras arquitectónicas. Enfatiza la naturaleza compleja del desempeño laboral y proporciona una base sólida para implementar estrategias de mejora en entornos laborales similar= es.

Tabla 2

Resultados del análi= sis de Kruskal-Wallis para factores que afectan rendimiento laboral<= /p>

Variable

χ²

gl

p

Tiempo

24.4

5=

< .001

Temperatura

29.9

5=

< .001

Suelo

35.4

5=

< .001

Cubierta

26.2

5=

< .001

Dificultad

25.5

5=

< .001

Peligro

38.1

5=

< .001

Continuidad

31.4

5=

< .001

Orden y aseo<= /span>

31.5

5=

< .001

Base de trabajo

33.6

5=

< .001

Tipicidad

35.3

5=

< .001

Tajo

36.5

5=

< .001

Herramienta

29.1

5=

< .001

Equipo=

22.3

5=

< .001

Tabla 2

Resultados del análisis de Kruskal-Wallis para factores que afect= an rendimiento laboral (continuación)

Variable=

χ²

gl

p

Mantenimiento=

22.5

5=

< .001

Suministro

26.6

5=

< .001

Elemento de protección

38.2

5=

< .001

Dirección

38.0

5=

< .001

Instrucción

33.3

5=

< .001

Seguimiento

33.3

5=

< .001

Calif. Maestro

27.5

5=

< .001

Aserg. Calidad

33.3

5=

< .001

Sit. Personal=

27.5

5=

< .001

Cansancio

21.2

5=

< .001

Habilidad

38.0

5=

< .001

Conocimiento<= /span>

38.0

5=

< .001

Capacitación<= /span>

27.5

5=

< .001

Luego de realizar el análisis de KW se procede con la conformación del modelo matemático basado en la regresión lineal múltiple. En este caso, el primer resultado evidenciado por el programa estadístico fueron las medidas de aju= ste del modelo. Como se puede observar en la tabla 3. Los datos muestran una fu= erte correlación (R =3D 0,968) y un alto nivel de previsibilidad (R² ajustado =3D 0,937), lo que sugiere que casi el 93,3% de la variación en el desempeño de= la tarea puede atribuirse a estas variables. La presencia de valores negativos= tanto en AIC (-99.1) como en BIC (-73.9) indica un ajuste muy favorable del model= o a los datos observados. Además, el valor bajo del error cuadrático medio (RMS= E) de 0,0255 resalta la alta precisión del modelo para predecir el desempeño laboral utilizando estas características.

Tabla 3

Medidas de ajuste de= l modelo

Modelo=

R<= i>

R² corregida<= /span>

AIC

BIC

RMSE

1

0.968

0.933

-99.1

-73.9

0.0255

El segundo hallazgo destacado, derivado del anális= is mediante regresión lineal, se centra en los coeficientes del modelo. Al exa= minar la Tabla 4, se observa que las variables independientes, evaluadas de forma individual, no exhiben una correlación significativa con el rendimiento de = los obreros. Este resultado se sustenta en los valores p calculados, los cuales superan el umbral de 0.05. En consecuencia, se infiere que, a nivel individ= ual, estas variables no poseen una influencia estadísticamente significativa en = el rendimiento laboral de los obreros en la actividad estudiada.

En relación con los estimadores, se destaca la pre= sencia de dos direcciones distintas para VI, según lo establecido por el modelo. Se observa que las VI de suelo, cubierta, peligro, actividad precedente, tipicidad, tajo, suministro, seguimiento y supervisión muestran estimadores negativos, indicando una relación inversa con el rendimiento laboral. Es de= cir, un incremento en estas variables se asocia con una disminución en el rendimiento. Por otro lado, las restantes variables presentan estimadores positivos, señalando una relación proporcional directa con el rendimiento, donde un aumento en estas VI se relaciona con un incremento en el rendimien= to laboral.

Tabla 4

Coeficientes del Modelo - Rendimiento

Predictor

Estimador

EE

t=

p=

Constante

1.7887=

1.2793=

1.398<= /span>

0.185<= /span>

Tiempo=

0.0863=

0.0849=

1.016<= /span>

0.328<= /span>

Temperatura

0.1174=

0.1100=

1.067<= /span>

0.305<= /span>

Suelo<= /span>

-0.6763

0.8077=

-0.837=

0.418<= /span>

Cubierta

-0.2479

0.1385=

-1.790=

0.097<= /span>

Dificultad

0.4917=

0.6010=

0.818<= /span>

0.428<= /span>

Peligro

-0.0212

0.1412=

-0.150=

0.883<= /span>

Interrupciones

0.8660=

0.9334=

0.928<= /span>

0.370<= /span>

Orden y aseo<= /span>

0.0400=

0.0773=

0.517<= /span>

0.614<= /span>

Actividades precedentes

-0.2083

0.1724=

-1.208=

0.248<= /span>

Tipicidad

-0.1278

0.0635=

-2.012=

0.065<= /span>

Tajo (Espacio de trabajo)<= /o:p>

-0.5193

0.7314=

-0.710=

0.490<= /span>

Herramienta

0.3254=

0.2292=

1.420<= /span>

0.179<= /span>

Equipo=

0.1263=

0.0933=

1.354<= /span>

0.199<= /span>

Suministro

-0.0177

0.0542=

-0.327=

0.749<= /span>

Seguimiento

-0.0256

0.0411=

-0.624=

0.543<= /span>

Supervisor (Maestro)=

-0.0312

0.0452=

-0.689=

0.503<= /span>

Una vez se calcularon l= os coeficientes se procede a usar el coeficiente estimador para armar el model= o a través de la formula general de la regresión lineal múltiple, y queda de la siguiente manera:

Y =3D 1.7887 + 0.0863*tiempo + 0.1174*temperatura - 0.6763*suelo -0.2479*cubi= erta + 0.4917*dificultad -0.0212*peligro + 0.8660*Interrupciones + 0.0400*Orden y = aseo - 0.2083*actividades precedentes -0.1278*tipicidad - 0.5193*tajo + 0.3254*herramienta + 0.1263*equipo - 0.0177*suministro - 0.0256*seguimiento= - 0.0312*supervisor

Con el propósito de validar= la efectividad del modelo propuesto, se lleva a cabo una comparación entre los rendimientos promedio reales alcanzados por los obreros, el rendimiento teó= rico proporcionado por el Gad Autónomo Desentralizado (GAD) de Cuenca que es de = 1.25 m2/hora y los rendimientos determinados mediante la aplicación d= e la fórmula de regresión lineal múltiple presentada previamente. Esta comparaci= ón se realizó para cada una de las obras analizadas y se presenta a continuaci= ón en la tabla 5. Como se evidencia en la tabla, las condiciones climáticas, la actividad laboral, el equipamiento disponible, la supervisión, las capacida= des individuales del trabajador y las condiciones laborales variaron entre las distintas obras. Esta variabilidad se reflejó en los niveles de rendimiento obtenidos, los cuales no fueron uniformes en todos los casos.

En la obra 1, los rendimien= tos oscilaron entre 1,22 y 1,27 m2/hora, mientras que en la obra 2 la variación fue entre 1,20 y 1,27 m2/hora. Por otro lado, en la ob= ra 3, los rendimientos variaron significativamente, entre 0,98 y 1,25 m2<= /sup>/hora, en la obra 4 la variación fue entre 0,99 y 1,10 m2/hora, en la o= bra 5 se observó una variación entre 1,01y 1,15 m2/hora, en la obra = 6, oscilaron entre 1,00 y 1,05 m2/hora. Por su parte, en la obra 7 = la variación fue entre 1,06 y 1,27  m<= sup>2/hora, en la obra 8 entre 1,22 y 1,27 m2/hora y en la obra 9 entre 1,10= y 1,27.

 Estos datos revelan dos aspectos importa= ntes. En primer lugar, que solo en 12 casos los rendimientos de los obreros alcanzaron o superaron el rendimiento teórico estipulado por el GAD de Cuen= ca, que es de 1,25 m2/hora, mientras que en los demás casos los rendimientos estuvieron por debajo de esta cifra. En segundo lugar, se obse= rva que el rendimiento de los obreros no sigue una tendencia lineal, y que incl= uso dentro de una misma edificación pueden existir variaciones significativas e= n el rendimiento.

Tabla 5

Comparación de rendimientos de obreros en función de indicadores

Obra

Obrero

Tiempo

Temperatura

Suelo

Cubierta

Dificultad

Peligro

Interrupciones

Orden y aseo

Actividades precedentes

Tipicidad

Tajo (Espacio de trabajo)

Herramienta

Equipo

Suministro

Seguimiento

Supervisor (Maestro)

Rendimiento real

Datos de rendimiento calculado

Rendimiento del GAD de Cuenca

1

1

4

5

4

4

3

2

4

3

4

3

5

3

3

5

4

4

1,27

1,26

1,25

 

Tabla 5

Comparación de rendimientos de obreros en función de indicadores (co= ntinuación)

Obra

Obrero

Tiempo

Temperatura

Suelo

Cubierta

Dificultad

Peligro

Interrupciones

Orden y aseo

Actividades precedentes

Tipicidad

Tajo (Espacio de trabajo)

Herramienta

Equipo

Suministro

Seguimiento

Supervisor (Maestro)

Rendimiento real

Datos de rendimiento calculado

Rendimiento del GAD de Cuenca

 

2

5

4

4

5

2

3

5

3

5

3

5

3

4

5

4

4

1,22

1,25

1,25

3

5

4

4

4

3

2

4

4

5

4

5

4

3

5

5

4

1,22

1,23

1,25

4

4

4

5

4

2

3

5

3

5

3

4

3

4

4

4

5

1,25

1,25

1,25

5

5

4

4

4

3

2

4

3

4

3

5

3

3

5

5

4

1,22

1,21

1,25

6

4

4

5

5

3

2

4

3

4

3

4

4

4

4

4

4

1,27

1,21

1,25

2

1

4

4

4

5

3

2

4

4

4

3

5

4

3

5

5

5

1,21

1,21

1,25

2

5

4

5

4

2

3

5

3

5

3

4

3

3

4

4

5

1,21

1,21

1,25

3

5

4

4

4

3

2

4

3

4

3

5

3

3

5

5

4

1,20

1,21

1,25

4

4

5

4

4

3

2

4

3

4

3

5

3

3

5

4

4

1,26

1,26

1,25

5

5

4

4

4

3

2

4

4

5

4

5

4

3

5

5

4

1,25

1,23

1,25

6

4

5

4

4

3

2

4

3

4

3

5

3

3

5

4

4

1,27

1,26

1,25

3

1

5

4

4

5

2

3

5

3

5

3

5

3

4

5

4

4

1,25

1,25

1,25

2

3

4

3

3

2

1

3

2

3

2

4

2

2

4

4

3

1,07

1,06

1,25

3

4

3

3

4

1

2

4

2

4

2

4

2

3

4

3

3

1,06

1,08

1,25

4

4

3

3

3

2

1

3

3

4

3

4

3

2

4

4

3

1,07

1,06

1,25

5

4

3

3

2

1

3

2

3

2

4

2

2

3

3

3

4

1,05

1,05

1,25

6

3

3

4

4

2

1

3

2

3

2

3

3

3

3

3

3

0,98

1,03

1,25

4

1

4

3

4

3

1

2

4

2

4

2

3

2

2

2

3

4

1,00

1,05

1,25

2

4

3

4

3

4

3

2

1

4

2

3

2

4

2

4

3

0,99

0,99

1,25

3

4

3

3

4

1

2

4

2

4

2

4

2

3

4

3

4

1,05

1,05

1,25

4

4

3

4

4

2

1

4

3

4

3

4

3

2

4

4

3

0,99

1,00

1,25

5

3

3

4

4

2

1

4

2

4

2

4

3

3

4

4

4

1,10

1,10

1,25

6

4

3

4

4

2

1

4

3

4

2

4

3

2

4

4

4

1,10

1,10

1,25

5

1

4

3

3

4

1

2

4

2

4

2

4

2

3

4

3

3

1,15

1,08

1,25

2

4

3

4

3

1

2

4

2

4

2

3

2

2

2

3

4

1,10

1,05

1,25

3

3

4

3

3

2

1

3

2

3

2

4

2

2

4

4

3

1,05

1,06

1,25

4

4

3

4

4

2

1

4

3

4

3

4

3

2

4

4

3

1,01

1,00

1,25

Tabla 5

Comparación de rendimientos de obreros en función de indicadores (co= ntinuación)

Obra

Obrero

Tiempo

Temperatura

Suelo

Cubierta

Dificultad

Peligro

Interrupciones

Orden y aseo

Actividades precedentes

Tipicidad

Tajo (Espacio de trabajo)

Herramienta

Equipo

Suministro

Seguimiento

Supervisor (Maestro)

Rendimiento real

Datos de rendimiento calculado

Rendimiento del GAD de Cuenca

 

5

4

3

4

4

2

1

4

3

4

3

4

3

2

4

4

3

1,01

1,00

1,25

6

1

3

4

3

3

2

1

3

2

3

2

4

2

2

4

4

3

1,05

1,06

1,25

2

4

3

3

3

2

1

3

3

4

3

4

3

2

4

4

3

1,05

1,06

1,25

3

4

3

3

2

1

3

2

3

2

4

2

2

3

3

3

4

1,05

1,05

1,25

4

3

3

4

4

2

1

3

2

3

2

3

3

3

3

3

3

1,00

1,03

1,25

5

4

3

4

3

1

2

4

2

4

2

3

2

2

2

3

4

1,00

1,05

1,25

6

4

3

4

3

4

3

2

1

4

2

3

2

4

2

4

3

1,00

0,99

1,25

7

1

5

4

4

4

3

2

4

4

5

4

5

4

3

5

5

4

1,25

1,23

1,25

2

4

5

4

4

3

2

4

3

4

3

5

3

3

5

4

4

1,27

1,26

1,25

3

5

4

4

5

2

3

5

3

5

3

5

3

4

5

4

4

1,25

1,25

1,25

4

3

4

3

3

2

1

3

2

3

2

4

2

2

4

4

3

1,07

1,06

1,25

5

4

3

3

4

1

2

4

2

4

2

4

2

3

4

3

3

1,06

1,08

1,25

6

4

3

3

3

2

1

3

3

4

3

4

3

2

4

4

3

1,07

1,06

1,25

8

1

4

5

4

4

3

2

4

3

4

3

5

3

3

5

4

4

1,27

1,26

1,25

2

5

4

4

5

2

3

5

3

5

3

5

3

4

5

4

4

1,22

1,22

1,25

3

5

4

4

4

3

2

4

4

5

4

5

4

3

5

5

4

1,22

1,23

1,25

4

4

4

5

4

2

3

5

3

5

3

4

3

4

4

4

5

1,25

1,25

1,25

9

1

5

4

4

4

3

2

4

3

4

3

5

3

3

5

5

4

1,22

1,21

1,25

2

4

4

5

5

3

2

4

3

4

3

4

4

4

4

4

4

1,27

1,21

1,25

3

4

4

4

5

3

2

4

4

4

3

5

4

3

5

5

5

1,21

1,21

1,25

4

3

3

4

4

2

1

4

2

4

2

4

3

3

4

4

4

1,10

1,10

1,25

Para ilustrar de manera más efectiva las fluctuaciones en los rendimientos de los obreros, se presenta en la figura 1 una representación gráfica que compara los rendimientos reales de los obreros, el rendimiento teórico establecido por el GAD de Cuenca y el rendimiento calculado mediant= e el modelo matemático.

Como se puede observar, el rendimiento real de los trabajadores no sigue una tendencia lineal, sino que fluctúa notablemente y en muchas ocasiones se sitúa por debajo del rendimie= nto esperado. Esto sugiere que, si un administrador de obra basa sus planificaciones únicamente en el rendimiento promedio teórico, es probable = que se enfrenten a retrasos o incumplimientos en los plazos de entrega.

En contraste, el rendimiento calculado mediante el modelo matemático se ajusta de manera satisfactoria a estas variaciones. Esto proporciona un promedio más realista del rendimiento que se puede esperar, teniendo en cuenta las diversas condiciones tanto externas como internas que afectan a los trabajadores.

Figura 1<= /span>

Comparación rendimie= ntos promedios reales y teóricos de obra 1

Conclusiones

·      =    Al explorar las correlaciones individuales entre las variables independientes en este estudio, se constató que ningún factor analizado ejerce, por sí solo, una influencia significativa en el rendimien= to laboral. No obstante, la conjunción de estos factores permite prever el rendimiento de un trabajador con una precisión de hasta el 93.3% según el análisis estadístico realizado (R2corregido). La similitud entre el rendimi= ento teórico estimado y el rendimiento promedio real de los obreros respalda la efectividad de la regresión lineal para anticipar estos rendimientos en fun= ción de los diversos factores evaluados.

·      =    El modelo matemático derivado de la regresión lineal, presentado en esta investigación, se revela como una herramienta potente pa= ra predecir el rendimiento de las cuadrillas de obreros en la Parroquia Cañaribamba, considerando tanto factores externos como internos de la obra. Esta aplicación podría resultar en la optimización significativa de los tie= mpos de ejecución y la reducción de los costos asociados a retrasos e incumplimientos de plazos en proyectos de construcción. Este modelo se puede extrapolar a otras parroquias de Cuenca que tengan un contexto similar y condiciones semejantes a las presentadas en este documento.

Conflicto de intereses

No existe conflicto de intereses

Agradecimiento

El presente artículo es parte del trabajo de investigación y titulación del Programa de Maestrías en Construcción con Mención en Administración de la Construcción Sustentable de la Universidad Católica de Cuenca, por ello agradecemos a todos y cada uno de los instruct= ores pertenecientes a los grupos de investigación; Ciudad, Ambiente, y Tecnología(CAT), y Sistemas embebidos y visión artificial en ciencias, Arquitecturas, Agropecuarias, Ambientales y Automática (SEVA4CA), por los conocimientos e información brindados para la elaboración del trabajo.=

Referencias bibliográficas

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Xu, C., Liu, J., Li, S., Wu, Z., y Chen, Y. F. (2021). Optimal brick layout of masonry walls based on intelligent evolutionary algorithm and building information modeling. Automation in Construction, 129, 103824. https://doi.org/10.1016/j.autcon.2021.103824

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El artículo queda= en propiedad de la revista y, por tanto, su publicación parcial y/o total en o= tro medio tiene que ser autorizado por el director de la Revista Conciencia Digital.

 

 

 

 

 


 

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ISSN: 2600-5859

Vol. 7 No. 1.3, pp. 49 – 68, marzo 2024<= /p>

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                                      Estructura Científica               Página 49 | 68    

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