Compared to general public and residential buildings, large public buildings are often difficult to construct and have a long construction period, creating greater construction energy consumption and carbon emissions on the one hand, while generating a large amount and many types of difficult-to-track process data on the other. As such, it is difficult to measure carbon emissions and analyze various influencing factors. By realizing the simple calculation of energy consumption and carbon emissions, as well as discerning the degree of influence of various factors based on the results of influencing factors research, it is of considerable practical significance to propose energy savings and emission reductions in a targeted manner. In view of the above, this work aimed to establish a more practical calculation method to measure energy consumption and carbon emissions in the construction of large public buildings, as well as to identify the multiple influencing factors related to energy consumption and carbon emissions during the construction process. To demonstrate the practicality of our approach, quantitative calculations are carried out for a new terminal building in a certain place and from the perspective of sustainable urban construction; thus, the driving factors of the traditional STIRPAT model are extended to seven. Based on the calculation results, a modified STIRPAT model is used to analyze the comparative study of impact factors, such as population and construction machinery performance, on energy consumption and carbon emission intensity. The results show the following: (1) The energy consumption value per square meter of this terminal building is 3.43 kgce/m2, and the average carbon emission per square meter is about 13.88 kgCO2/m2, which is much larger than the national average of 6.96 kgCO2/m2, and (2) the type of energy used in the construction process has the greatest degree of influence on energy consumption and carbon emission, and the local GDP, population factor, construction machinery performance specifications, and shift usage also show a positive correlation with the growth of total energy consumption and carbon emissions. Moreover, while the government’s continuous investment in energy conservation and environmental protection has reduced the total energy consumption and carbon emissions in construction, there is still considerable room for improvement. Finally, according to the results, we provide theoretical references and constructive suggestions for the low-carbon construction of large public buildings in the construction stage. Thus, the results of our study will allow policy makers to formulate appropriate policies.

Terrorist attacks or contact explosions could trigger severe damage to structural components or even cause the local or global collapse of the structure in civil engineering and infrastructures. Single‐steel‐concrete (SSC) slabs consist of a concrete core and one steel faceplate combined with shear studs or tie bars that can be applied in resisting explosion or blast load. This paper experimentally and numerically studies the blast performance of the scale SSC specimens under a contact explosion. The damage modes, deflections, and acceleration response of the SSC specimens are obtained and compared by experimental and numerical methods. And then the influences of the explosive, the shear stud length, and the steel plate thickness on the damage modes, acceleration, and displacement response are studied parametrically. It is observed that both of the concrete layers in SSC slabs have crater failure at the impacted surface and steel plates occur local deformation or buckling. No penetrability failure generates in the slabs because of the existence of the steel plate, which can improve the blast resistance and decrease the maximum deflections of the specimens. Finally, an empirical equation is developed to estimate the midspan displacement of the slab using the multivariable regression method (MNRM).

With the continuous improvement of building energy-conservation requirements, both traditional concrete external insulation and internal insulation have been unable to meet energy-saving needs. In order to meet the demands of building energy-saving in the new era, new precast concrete external-wall-insulation technology should be developed. In this study, a bending static test and numerical simulation were carried out to evaluate the influence of the thickness of inner concrete wythe and insulation and the length of plate-type shear connectors on the cracking condition, bearing capacity and composite degree of a precast ceramsite-concrete-insulated sandwich panel (PCCISP) under the outside-plane load. The results show that the failure modes of four precast ceramsite-concrete-insulated sandwich panels were all ductile failure of the concrete flexural members. The ultimate bearing capacity of the PCCISP decreased with the decrease in the thickness of the inner concrete wythe. Reducing the thickness of insulation had no significant influence on the ultimate bearing capacity. When the thickness of insulation was reduced by 30%, the composite degree of rigidity and bearing capacity of the PCCISP were increased by 8.85% and 2.67%, respectively. Increasing the length of the plate-type shear connector slightly increased the ultimate bearing capacity, but it had no obvious influence on the rigidity and bearing capacity composite degree.

To assist in addressing the problem where an aluminum alloy formwork (AAF) deforms more greatly under the action of lateral pressure and therefore does not meet the requirements of plaster-free engineering, we propose a method for determining the geometric parameters of this formwork based on a PSO algorithm and BP neural network with ABAQUS as the platform. The influence of six geometric parameters of the formwork on the maximum deflection value of the panel under the action of lateral pressure is studied using finite element analysis. The maximum deflection value of the panel is used as the index, and the influence of each factor is analyzed with an orthogonal test, and a set of optimal geometric parameters is obtained via extreme difference analysis and analysis of variance. The sample data are obtained via finite element simulation, and the PSO-BP neural network model is established using the six factors of the orthogonal test as input values and the maximum deflection of the panel as the output value, and the optimal geometric parameters are optimized using the PSO algorithm. The results indicate that the maximum deflection for the panel in the orthogonal scheme is 1.446 mm. The PSO-BP neural network prediction model demonstrates greater accuracy and a 31.74% reduction in running time compared to the BP neural network prediction model. The optimized PSO-BP neural network prediction model scheme reveals a maximum panel deflection of 1.296 mm, a 10.37% decrease compared to the orthogonal solution. These findings offer technical guidance and a foundation for optimizing AAF designs, presenting practical applications.