End-to-end GRU model for construction crew management

Crew management is critical towards improving construction task productivity. Traditional methods for crew management on-site are heavily dependent on the experience of site managers. This paper proposes an end-to-end Gated Recurrent Units (GRU) based framework which provides site managers a more reliable and robust method for managing crews and improving productivity. The proposed framework predicts task productivity of all possible crew combinations, within a given size, from the pool of available workers using an advanced GRU model. The model has been trained with an existing database of masonry work and was found to outperform other machine learning models. The results of the framework suggest which crew combinations have the highest predicted productivity and can be used by superintendents and project managers to improve construction task productivity and better plan future projects

Performance indicators in the construction industry: a study with Portuguese companies

This paper aims to study the use of performance indicators (PIs) by business managers in the civil construction sector in a region that encompasses eight districts located in the north of Portugal. Through the literature review, it was possible to gather a list of twenty potential PIs used in this sector. Subsequently, a questionnaire was applied to a sample of construction companies from this region. A statistical analysis of the data collected allowed to identify the twelve most used and most important PIs for the companies surveyed. The results indicate that the companies involved in this study mainly use the traditional financial measures, however, recognize that non-financial measures, namely the customer satisfaction index, employee satisfaction, internal customer satisfaction index, and the training of employees, are increasingly important for the success of companies. The study also analysis the criteria to select PIs, its main benefits, and difficulties faced by companies on their usage.INCT-EN - Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção(UID/CEC/00319/2019

Implementing supply chain partnering in the construction industry

Although much research has been conducted about advantages and challenges for supply chain partnering (SCP) in the construction sector, focus has been mostly on formal aspects of implementation within organizations. Understanding social aspects, however, might be just as crucial to implementation of SCP as understanding managerial and intraorganizational dynamics. Therefore, this paper presents the results of a study in which a work floor professional together with a researcher tried to contribute to the implementation of SCP within the maintenance and refurbishment processes of a Dutch housing association. The results showed that stakeholders could not come to shared understanding of strategic needs, and that that pattern influences and was influenced by social aspects such as leadership and trust, which confirms the importance of explicit attention for social interactions at work floor level for successful implementation of supply chain partnering

Corporate Social Responsibility as a Vehicle for Ensuring the Survival of Construction SMEs.

Política de acceso abierto tomada de: https://v2.sherpa.ac.uk/id/publication/3449The purpose of this article is to analyze the effect of corporate social responsibility (CSR) on performance through the mediating role of job satisfaction and innovation in a sample of 503 Spanish SMEs construction. Developing a partial least squares structural equation modeling (PLS-SEM) to test our hypotheses, the results provide evidence that performance is influenced by CSR, job satisfaction, and innovation. These effects are not only direct and positive but, indirect effects which allow the positive effects of CSR to be enhanced are also obtained. This article by empirically examining the relationship between CSR, job satisfaction, innovation, and performance provides an essential contribution to the literature by filling a gap related to the direct effect of CSR on performance, and the indirect effect by the mediation of job satisfaction and innovation. The findings show significant implications for policymakers and managers. The findings can help managers to invest in CSR, which, by improving the well-being of their employees and the innovative capacity of their company, will lead to better performance and the capacity to adapt to the current changing environment. In addition, our results provide evidence that SMEs with fewer resources should be able to count on public support to carry out CSR practices

The concrete effect on Co2 emissions

Mestrado Bolonha em ManagementThis thesis explores the impact of the concrete industry on Co2 emissions and examines sustainable practices to reduce this impact. The concrete industry is a major contributor to global CO2 emissions, with the production of cement, one of the crucial ingredients in concrete production, being a significant source of greenhouse gases as global demand for concrete increases, so does the need for more sustainable production practices. The study examines the current state of the concrete industry globally and in Portugal, as well as regulatory frameworks for reducing carbon dioxide emissions. Sustainable practices for concrete production, with a specific focus on some solutions from Solidia, CarbonCure, and Secil. The findings suggest that while the concrete industry remains a significant contributor to CO2 emissions, promising developments in low-carbon clinker production and carbon capture technologies indicate a potential path toward greater sustainability. However, there are many sustainable practices being developed and implemented to reduce the industry's environmental impact. Overall, this work highlights the importance of continued research and regulation to ensure that the concrete industry can play a role in mitigating climate change. Finally, while the concrete industry remains a significant contributor to global CO2 emissions, we are making some progress toward achieving sustainability. The industry is increasingly adopting sustainable practices, and with continued research and development, it is expected to see even more progress in reducing the industry's environmental impact in the future.Esta tese explora o impacto da indústria do betão nas emissões de dióxido de carbono na indústria do betão e analisa práticas sustentáveis para reduzir esse impacto. A indústria do betão é uma das principais contribuintes para as emissões globais de CO2, sendo a produção de cimento, um dos ingredientes mais importantes na produção de betão, uma fonte significativa de gases de efeito estufa e, à medida que a procura global por betão aumenta, também aumenta a necessidade de práticas de produção mais sustentáveis. O estudo analisa o estado atual da indústria do betão a nível mundial e em Portugal, bem como os quadros regulamentares para a redução das emissões de dióxido de carbono. Foram exploradas práticas sustentáveis para o betão e o atual panorama da indústria em Portugal, com destaque para algumas soluções da Solidia, CarbonCure e Secil. As descobertas sugerem que, embora a indústria do betão continue contribuindo significativamente para as emissões de CO2, desenvolvimentos promissores na produção de clínquer com baixo teor de carbono e tecnologias de captura de carbono indicam um caminho potencial para uma maior sustentabilidade. No entanto, existem muitas práticas sustentáveis a ser desenvolvidas e implementadas para reduzir o impacto ambiental da indústria. No geral, este trabalho destaca a importância da pesquisa e regulamentação contínuas para garantir que a indústria do betão possa desempenhar um papel na mitigação das mudanças climáticas. Por fim, embora a indústria do betão continue a contribuir significativamente para as emissões globais de CO2, estão a ser feitos alguns progressos no sentido de alcançar a sustentabilidade. A indústria está a adotar cada vez mais práticas sustentáveis e, com pesquisa e desenvolvimento contínuos, são esperados ainda mais progressos na redução do impacto ambiental da indústria no futuro.info:eu-repo/semantics/publishedVersio

Exploring Leadership Strategies to Maximize Profitability in the Nigerian Housing Sector

The collapse of construction companies in the Nigerian housing sector continues unabated, even in the face of 17 million housing deficits. Many construction company leaders believe that lack of business opportunities and the recent world economic decline have been responsible for the collapse. This situation has resulted in limited business activities for 80% of the Nigerian construction companies. This multiple case study explored the strategies used by leaders to maximize profitability in the Nigerian housing sector. The RBV and Porter\u27s model of competition provided the conceptual framework for the study. Findings were based on detailed reviews of the policies and procedures of the companies, coupled with semi-structured face-to-face interviews with 5 leaders of construction companies that have successfully completed and currently involved in several housing projects in 2 southwestern states in Nigeria. The research question examined the strategies construction company leaders used to maximize profitability in the Nigerian housing sector. Four themes representing strategy categories emerged from thematic analysis: planning, human capital development, leadership factor, and organizational location. The key outcomes from the findings include the need to plan with the available resources, employ and invest in competent staff, increase leadership influence, and improve knowledge of the business environment. The implication for social change includes a potential reduction in unemployment in Nigeria. Profitable organizations will construct more affordable housing through collaboration with public authority, and more low-income earners will be able to afford to live in a decent environment, thus reducing the populations of slum dwellers in the country

Quais as principais características organizacionais das empresas dos diferentes segmentos da construção civil?

Neste artigo os autores se propõem a identificar os fatores que definem as principais características organizacionais de empresas do subsetor da construção civil, localizadas em Curitiba e região metropolitana, assim como verificar as diferenças existentes entre os cinco segmentos de atividade mais relevantes desse mercado: residencial, industrial e comercial, infraestrutura, serviços especializados e construção industrializada. Para tanto, foi desenvolvida uma pesquisa exploratória e de natureza quantitativa, em nível de mestrado. O método survey foi escolhido como procedimento principal para este estudo, permitindo a obtenção de dados primários por meio da aplicação de um questionário em um grupo de 118 empresas. Esse questionário procurou identificar o perfil da organização e as características organizacionais das empresas avaliadas. Os dados foram analisados com a aplicação de técnicas multivariadas de análise fatorial e análise discriminante. A análise fatorial evidenciou as estratégias de melhoria, o comportamento do indivíduo no trabalho, a estrutura de funcionamento, a dinâmica de crescimento, o estilo de gestão, as relações interpessoais e o posicionamento da empresa perante o mercado como fatores determinantes. A análise discriminante apontou significativa homogeneidade no desenvolvimento organizacional das empresas que representam os diferentes segmentos de atividade

Quais as principais características organizacionais das empresas dos diferentes segmentos da construção civil?

Resumo Neste artigo os autores se propõem a identificar os fatores que definem as principais características organizacionais de empresas do subsetor da construção civil, localizadas em Curitiba e região metropolitana, assim como verificar as diferenças existentes entre os cinco segmentos de atividade mais relevantes desse mercado: residencial, industrial e comercial, infraestrutura, serviços especializados e construção industrializada. Para tanto, foi desenvolvida uma pesquisa exploratória e de natureza quantitativa, em nível de mestrado. O método survey foi escolhido como procedimento principal para este estudo, permitindo a obtenção de dados primários por meio da aplicação de um questionário em um grupo de 118 empresas. Esse questionário procurou identificar o perfil da organização e as características organizacionais das empresas avaliadas. Os dados foram analisados com a aplicação de técnicas multivariadas de análise fatorial e análise discriminante. A análise fatorial evidenciou as estratégias de melhoria, o comportamento do indivíduo no trabalho, a estrutura de funcionamento, a dinâmica de crescimento, o estilo de gestão, as relações interpessoais e o posicionamento da empresa perante o mercado como fatores determinantes. A análise discriminante apontou significativa homogeneidade no desenvolvimento organizacional das empresas que representam os diferentes segmentos de atividade

Comprehensive assessment model on accident situations of the construction industry in China: a macro-level perspective

As one of the most high-risk sections, the construction industry has traditionally been the research hotspot. Yet little attention has been paid to macro-level accident situations of the entire industry. Therefore, this study develops a comprehensive assessment model on accident situations of Chinese building industry, aiming at assisting the government to better understand and improve accident situations of the entire industry. Based on China conditions, six indicators related to accident situations are firstly selected to establish an indicator system; then structure entropy weight method is proposed to determine indicator weighs, with dynamic classification method to explore the characteristics of accident situations. The results demonstrate that the provinces with poor accident situations account for 53% of all provinces, and they are mainly distributed in the central and western regions of China where there exist the underdeveloped economy. Meanwhile, some provinces experience poor accident situations that could be out-of-control, especially for Hebei. Provinces in the southeastern and northeastern regions of China perform relatively well, but they still have much improvement room for accident situations. The findings validate the rationality of the developed model and can provide valuable insights of safety regulation strategies for the government from the macro-level perspective. First published online 17 December 201

Developing a Machine Learning based Systematic Investment Startegy: A case study for the Construction Industry

In this research work, an end-to-end systematic investment strategy based on machine learning models and leveraging the construction industry operational and management practices knowledge, is implemented. First, a literature research in the field of behavioral finance is done, presenting the current state of the knowledge and trends in the industry. A suitable investment opportunity exploiting prevailing market inefficiencies around earnings announcements is identified. Second, an extensive literature research is performed identifying the most relevant characteristics of construction companies’ operations and major risk factors they are exposed to. These insights are used to engineer a set of relevant variables. Third, advanced statistical techniques are used to select the most relevant subset of features, which includes market and analysts’ expectation data, macroeconomic indicators, the delay in reporting earnings, and the most important financial dimensions for construction firms. Fourth, the earnings’ surprise classification problem is characterized by a class imbalance and asymmetric misclassification costs. These issues are a consequence of the desired business application, and are addressed by selecting an appropriate evaluation metric. Additionally, considerations on the temporal dimension and generative process of the data are made to select an appropriate validation scheme. Five different state-of-the-art machine learning algorithms are considered: a multinomial logistic regression, a bagging classifier, a random forest, an XGBoost and a linear Support Vector Machine. The multinomial logistic regression is found to be the most suitable model, exhibiting a bias towards predicting positive earnings’ surprises over the rest of classes. The firm size, and the profitability and valuation measures, portrayed by the Return on Assets and Enterprise Value multiples, are found to be the most important variables when predicting earnings surprises. To conclude, the systematic investment strategy based on the investment signals produced by the selected machine learning model is back-tested, being the performance of the long-short portfolio driven by the positive surprise one as a consequence of the selected model bias. Keywords: Quantitative Investing, Machine Learning, Behavioral Financ