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With the rapid development of construction industry and the large discharge of construction waste, there are many environmental problems. In order to formulate and implement the strategy of reducing management at the project level, it is necessary to estimate the amount of construction waste production accurately. Based on two methods, the building area method and the BIM method are used to estimate the amount of construction waste generated by a new residence, and a comparative model is established. According to the results, the method based on BIM can accurately estimate the amount of residential construction waste, improve the management level of residential construction waste and reduce environmental pollution.

A new hybrid algorithm is proposed by incorporating Harris Hawks Optimization with Marine Predators algorithm and dynamic Opposition-based learning, namely DMPA-HHO. In the algorithm, the problem is addressed that Harris Hawks Optimization (HHO) tends to fall into local optima and low accuracy of the solution. Dynamic Opposite Learning (DOL) improves the swarm diversity and swarm quality, and enhances the global search capability and search accuracy. HHO and the Marine Predators Algorithm (MPA) are blended to enhance the progressive rapid dives of the Harris hawk flock, effectively improving the algorithm's exploitation capabilities. DMPA-HHO uses the FADs’ effect of the MPA to increase the possibility of individuals escaping from the local optimum solution when the search falls into the local optimal solution. Compared with others on several benchmark functions, the DMPA-HHO algorithm has a better search accuracy and a stronger ability to avoid trapping in local optima.

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