Application of Machine Learning to predict the mechanical properties of high strength steel at elevated temperatures based on the chemical composition

This paper presents the results obtained using Machine Learning (ML) algorithms to predict the mechanical properties, including ultimate tensile strength, yield strength, 0.2% proof strength and elastic modulus, of high strength steel plate material at elevated temperatures. High strength steels are increasingly used in several areas of construction offering efficient structural solutions with a high strength-to-weight ratio. Safe fire design of these structures relies heavily on accurate prediction of mechanical properties of the material with temperature. The data on elevated temperature mechanical properties collected from the literature experimental tests show a high degree of scatter, implying that they are influenced significantly by various factors, most notably the testing method, manufacturing process and chemical composition. However, the current methods for predicting the mechanical properties of high strength steels at elevated temperatures by using ‘reduction factors’ as adopted by the structural design codes do not consider these effects and may lead to inaccurate predictions. To overcome these deficiencies, a ML-based prediction method that uses temperature and chemical composition as input parameters is developed in this paper. Deep Neural Networks are trained and validated on the basis of elevated temperature material data collated from the literature test programmes. The analysis of the results show that the trained algorithm gives an excellent correlation coefficient with very small error value in predicting the strength and stiffness reduction factors of HSS

Application of Machine Learning to predict the mechanical properties of high strength steel at elevated temperatures based on the chemical composition

This paper presents the results obtained using Machine Learning (ML) algorithms to predict the mechanical properties, including ultimate tensile strength, yield strength, 0.2% proof strength and elastic modulus, of high strength steel plate material at elevated temperatures. High strength steels are increasingly used in several areas of construction offering efficient structural solutions with a high strength-to-weight ratio. Safe fire design of these structures relies heavily on accurate prediction of mechanical properties of the material with temperature. The data on elevated temperature mechanical properties collected from the literature experimental tests show a high degree of scatter, implying that they are influenced significantly by various factors, most notably the testing method, manufacturing process and chemical composition. However, the current methods for predicting the mechanical properties of high strength steels at elevated temperatures by using ‘reduction factors’ as adopted by the structural design codes do not consider these effects and may lead to inaccurate predictions. To overcome these deficiencies, a ML-based prediction method that uses temperature and chemical composition as input parameters is developed in this paper. Deep Neural Networks are trained and validated on the basis of elevated temperature material data collated from the literature test programmes. The analysis of the results show that the trained algorithm gives an excellent correlation coefficient with very small error value in predicting the strength and stiffness reduction factors of HSS.</p

Construction and energy aspects of affordable housing developments for formal settlements

Nearly one quarter of the world’s urban population lives in informal settlements or encampments, most in developing countries but increasingly also in the most affluent countries. Many residents live in overcrowded, insecure dwellings, without water and sanitation, fearful of eviction and subject to preventable life-threatening illnesses. UN Sustainable Development Goal 11: “Make cities and human settlements inclusive, safe, resilient and sustainable” is committed to ensure access for all to adequate, safe and affordable housing and upgrade slums by 2030. There is therefore an urgent need for more affordable and permanent housing to be developed. This paper presents a review of the construction and energy aspects of affordable housing developments for informal settlement dwellers. The conditions of existing informal settlements in Global South countries have been researched and various case studies of informal settlement upgrading programmes are presented. The potentials of solar energy technologies in development of green affordable houses in case study countries Uganda and Indonesia are assessed

Construction and energy aspects of affordable housing developments for formal settlements

Nearly one quarter of the world’s urban population lives in informal settlements or encampments, most in developing countries but increasingly also in the most affluent countries. Many residents live in overcrowded, insecure dwellings, without water and sanitation, fearful of eviction and subject to preventable life-threatening illnesses. UN Sustainable Development Goal 11: “Make cities and human settlements inclusive, safe, resilient and sustainable” is committed to ensure access for all to adequate, safe and affordable housing and upgrade slums by 2030. There is therefore an urgent need for more affordable and permanent housing to be developed. This paper presents a review of the construction and energy aspects of affordable housing developments for informal settlement dwellers. The conditions of existing informal settlements in Global South countries have been researched and various case studies of informal settlement upgrading programmes are presented. The potentials of solar energy technologies in development of green affordable houses in case study countries Uganda and Indonesia are assessed